evaluation

This document provides guidance on creating comprehensive evaluations for MCP servers.

MCP Server Evaluation Guide

Overview

This document provides guidance on creating comprehensive evaluations for MCP servers. Evaluations test whether LLMs can effectively use your MCP server to answer realistic, complex questions using only the tools provided.

Quick Reference

Evaluation Requirements

Create 10 human-readable questions
Questions must be READ-ONLY, INDEPENDENT, NON-DESTRUCTIVE
Each question requires multiple tool calls (potentially dozens)
Answers must be single, verifiable values
Answers must be STABLE (won't change over time)

Output Format

<evaluation>
   <qa_pair>
      <question>Your question here</question>
      <answer>Single verifiable answer</answer>
   </qa_pair>
</evaluation>

Purpose of Evaluations

The measure of quality of an MCP server is NOT how well or comprehensively the server implements tools, but how well these implementations (input/output schemas, docstrings/descriptions, functionality) enable LLMs with no other context and access ONLY to the MCP servers to answer realistic and difficult questions.

Evaluation Overview

Create 10 human-readable questions requiring ONLY READ-ONLY, INDEPENDENT, NON-DESTRUCTIVE, and IDEMPOTENT operations to answer. Each question should be:

Realistic
Clear and concise
Unambiguous
Complex, requiring potentially dozens of tool calls or steps
Answerable with a single, verifiable value that you identify in advance

Question Guidelines

Core Requirements

Questions MUST be independent

Each question should NOT depend on the answer to any other question
Should not assume prior write operations from processing another question

Questions MUST require ONLY NON-DESTRUCTIVE AND IDEMPOTENT tool use

Should not instruct or require modifying state to arrive at the correct answer

Questions must be REALISTIC, CLEAR, CONCISE, and COMPLEX

Must require another LLM to use multiple (potentially dozens of) tools or steps to answer

Complexity and Depth

Questions must require deep exploration

Consider multi-hop questions requiring multiple sub-questions and sequential tool calls
Each step should benefit from information found in previous questions

Questions may require extensive paging

May need paging through multiple pages of results
May require querying old data (1-2 years out-of-date) to find niche information
The questions must be DIFFICULT

Questions must require deep understanding

Rather than surface-level knowledge
May pose complex ideas as True/False questions requiring evidence
May use multiple-choice format where LLM must search different hypotheses

Questions must not be solvable with straightforward keyword search

Do not include specific keywords from the target content
Use synonyms, related concepts, or paraphrases
Require multiple searches, analyzing multiple related items, extracting context, then deriving the answer

Tool Testing

Questions should stress-test tool return values

May elicit tools returning large JSON objects or lists, overwhelming the LLM
Should require understanding multiple modalities of data:
IDs and names
Timestamps and datetimes (months, days, years, seconds)
File IDs, names, extensions, and mimetypes
URLs, GIDs, etc.
Should probe the tool's ability to return all useful forms of data

Questions should MOSTLY reflect real human use cases

The kinds of information retrieval tasks that HUMANS assisted by an LLM would care about

Questions may require dozens of tool calls

This challenges LLMs with limited context
Encourages MCP server tools to reduce information returned

Include ambiguous questions

May be ambiguous OR require difficult decisions on which tools to call
Force the LLM to potentially make mistakes or misinterpret
Ensure that despite AMBIGUITY, there is STILL A SINGLE VERIFIABLE ANSWER

Stability

Questions must be designed so the answer DOES NOT CHANGE

Do not ask questions that rely on "current state" which is dynamic
For example, do not count:
Number of reactions to a post
Number of replies to a thread
Number of members in a channel

DO NOT let the MCP server RESTRICT the kinds of questions you create

Create challenging and complex questions
Some may not be solvable with the available MCP server tools
Questions may require specific output formats (datetime vs. epoch time, JSON vs. MARKDOWN)
Questions may require dozens of tool calls to complete

Answer Guidelines

Verification

Answers must be VERIFIABLE via direct string comparison

If the answer can be re-written in many formats, clearly specify the output format in the QUESTION
Examples: "Use YYYY/MM/DD.", "Respond True or False.", "Answer A, B, C, or D and nothing else."
Answer should be a single VERIFIABLE value such as:
User ID, user name, display name, first name, last name
Channel ID, channel name
Message ID, string
URL, title
Numerical quantity
Timestamp, datetime
Boolean (for True/False questions)
Email address, phone number
File ID, file name, file extension
Multiple choice answer
Answers must not require special formatting or complex, structured output
Answer will be verified using DIRECT STRING COMPARISON

Readability

Answers should generally prefer HUMAN-READABLE formats

Examples: names, first name, last name, datetime, file name, message string, URL, yes/no, true/false, a/b/c/d
Rather than opaque IDs (though IDs are acceptable)
The VAST MAJORITY of answers should be human-readable

Stability

Answers must be STABLE/STATIONARY

Look at old content (e.g., conversations that have ended, projects that have launched, questions answered)
Create QUESTIONS based on "closed" concepts that will always return the same answer
Questions may ask to consider a fixed time window to insulate from non-stationary answers
Rely on context UNLIKELY to change
Example: if finding a paper name, be SPECIFIC enough so answer is not confused with papers published later

Answers must be CLEAR and UNAMBIGUOUS

Questions must be designed so there is a single, clear answer
Answer can be derived from using the MCP server tools

Diversity

Answers must be DIVERSE

Answer should be a single VERIFIABLE value in diverse modalities and formats
User concept: user ID, user name, display name, first name, last name, email address, phone number
Channel concept: channel ID, channel name, channel topic
Message concept: message ID, message string, timestamp, month, day, year

Answers must NOT be complex structures

Not a list of values
Not a complex object
Not a list of IDs or strings
Not natural language text
UNLESS the answer can be straightforwardly verified using DIRECT STRING COMPARISON
And can be realistically reproduced
It should be unlikely that an LLM would return the same list in any other order or format

Evaluation Process

Step 1: Documentation Inspection

Read the documentation of the target API to understand:

Available endpoints and functionality
If ambiguity exists, fetch additional information from the web
Parallelize this step AS MUCH AS POSSIBLE
Ensure each subagent is ONLY examining documentation from the file system or on the web

Step 2: Tool Inspection

List the tools available in the MCP server:

Inspect the MCP server directly
Understand input/output schemas, docstrings, and descriptions
WITHOUT calling the tools themselves at this stage

Step 3: Developing Understanding

Repeat steps 1 & 2 until you have a good understanding:

Iterate multiple times
Think about the kinds of tasks you want to create
Refine your understanding
At NO stage should you READ the code of the MCP server implementation itself
Use your intuition and understanding to create reasonable, realistic, but VERY challenging tasks

Step 4: Read-Only Content Inspection

After understanding the API and tools, USE the MCP server tools:

Inspect content using READ-ONLY and NON-DESTRUCTIVE operations ONLY
Goal: identify specific content (e.g., users, channels, messages, projects, tasks) for creating realistic questions
Should NOT call any tools that modify state
Will NOT read the code of the MCP server implementation itself
Parallelize this step with individual sub-agents pursuing independent explorations
Ensure each subagent is only performing READ-ONLY, NON-DESTRUCTIVE, and IDEMPOTENT operations
BE CAREFUL: SOME TOOLS may return LOTS OF DATA which would cause you to run out of CONTEXT
Make INCREMENTAL, SMALL, AND TARGETED tool calls for exploration
In all tool call requests, use the limit parameter to limit results (<10)
Use pagination

Step 5: Task Generation

After inspecting the content, create 10 human-readable questions:

An LLM should be able to answer these with the MCP server
Follow all question and answer guidelines above

Output Format

Each QA pair consists of a question and an answer. The output should be an XML file with this structure:

<evaluation>
   <qa_pair>
      <question>Find the project created in Q2 2024 with the highest number of completed tasks. What is the project name?</question>
      <answer>Website Redesign</answer>
   </qa_pair>
   <qa_pair>
      <question>Search for issues labeled as "bug" that were closed in March 2024. Which user closed the most issues? Provide their username.</question>
      <answer>sarah_dev</answer>
   </qa_pair>
   <qa_pair>
      <question>Look for pull requests that modified files in the /api directory and were merged between January 1 and January 31, 2024. How many different contributors worked on these PRs?</question>
      <answer>7</answer>
   </qa_pair>
   <qa_pair>
      <question>Find the repository with the most stars that was created before 2023. What is the repository name?</question>
      <answer>data-pipeline</answer>
   </qa_pair>
</evaluation>

Evaluation Examples

Good Questions

Example 1: Multi-hop question requiring deep exploration (GitHub MCP)

<qa_pair>
   <question>Find the repository that was archived in Q3 2023 and had previously been the most forked project in the organization. What was the primary programming language used in that repository?</question>
   <answer>Python</answer>
</qa_pair>

This question is good because:

Requires multiple searches to find archived repositories
Needs to identify which had the most forks before archival
Requires examining repository details for the language
Answer is a simple, verifiable value
Based on historical (closed) data that won't change

Example 2: Requires understanding context without keyword matching (Project Management MCP)

<qa_pair>
   <question>Locate the initiative focused on improving customer onboarding that was completed in late 2023. The project lead created a retrospective document after completion. What was the lead's role title at that time?</question>
   <answer>Product Manager</answer>
</qa_pair>

This question is good because:

Doesn't use specific project name ("initiative focused on improving customer onboarding")
Requires finding completed projects from specific timeframe
Needs to identify the project lead and their role
Requires understanding context from retrospective documents
Answer is human-readable and stable
Based on completed work (won't change)

Example 3: Complex aggregation requiring multiple steps (Issue Tracker MCP)

<qa_pair>
   <question>Among all bugs reported in January 2024 that were marked as critical priority, which assignee resolved the highest percentage of their assigned bugs within 48 hours? Provide the assignee's username.</question>
   <answer>alex_eng</answer>
</qa_pair>

This question is good because:

Requires filtering bugs by date, priority, and status
Needs to group by assignee and calculate resolution rates
Requires understanding timestamps to determine 48-hour windows
Tests pagination (potentially many bugs to process)
Answer is a single username
Based on historical data from specific time period

Example 4: Requires synthesis across multiple data types (CRM MCP)

<qa_pair>
   <question>Find the account that upgraded from the Starter to Enterprise plan in Q4 2023 and had the highest annual contract value. What industry does this account operate in?</question>
   <answer>Healthcare</answer>
</qa_pair>

This question is good because:

Requires understanding subscription tier changes
Needs to identify upgrade events in specific timeframe
Requires comparing contract values
Must access account industry information
Answer is simple and verifiable
Based on completed historical transactions

Poor Questions

Example 1: Answer changes over time

<qa_pair>
   <question>How many open issues are currently assigned to the engineering team?</question>
   <answer>47</answer>
</qa_pair>

This question is poor because:

The answer will change as issues are created, closed, or reassigned
Not based on stable/stationary data
Relies on "current state" which is dynamic

Example 2: Too easy with keyword search

<qa_pair>
   <question>Find the pull request with title "Add authentication feature" and tell me who created it.</question>
   <answer>developer123</answer>
</qa_pair>

This question is poor because:

Can be solved with a straightforward keyword search for exact title
Doesn't require deep exploration or understanding
No synthesis or analysis needed

Example 3: Ambiguous answer format

<qa_pair>
   <question>List all the repositories that have Python as their primary language.</question>
   <answer>repo1, repo2, repo3, data-pipeline, ml-tools</answer>
</qa_pair>

This question is poor because:

Answer is a list that could be returned in any order
Difficult to verify with direct string comparison
LLM might format differently (JSON array, comma-separated, newline-separated)
Better to ask for a specific aggregate (count) or superlative (most stars)

Verification Process

After creating evaluations:

Examine the XML file to understand the schema
Load each task instruction and in parallel using the MCP server and tools, identify the correct answer by attempting to solve the task YOURSELF
Flag any operations that require WRITE or DESTRUCTIVE operations
Accumulate all CORRECT answers and replace any incorrect answers in the document
Remove any <qa_pair> that require WRITE or DESTRUCTIVE operations

Remember to parallelize solving tasks to avoid running out of context, then accumulate all answers and make changes to the file at the end.

Tips for Creating Quality Evaluations

Think Hard and Plan Ahead before generating tasks
Parallelize Where Opportunity Arises to speed up the process and manage context
Focus on Realistic Use Cases that humans would actually want to accomplish
Create Challenging Questions that test the limits of the MCP server's capabilities
Ensure Stability by using historical data and closed concepts
Verify Answers by solving the questions yourself using the MCP server tools
Iterate and Refine based on what you learn during the process

Running Evaluations

After creating your evaluation file, you can use the provided evaluation harness to test your MCP server.

Setup

Install Dependencies

``bash pip install -r scripts/requirements.txt ``

Or install manually: ``bash pip install anthropic mcp ``

Set API Key

``bash export ANTHROPIC_API_KEY=your_api_key_here ``

Evaluation File Format

Evaluation files use XML format with <qa_pair> elements:

<evaluation>
   <qa_pair>
      <question>Find the project created in Q2 2024 with the highest number of completed tasks. What is the project name?</question>
      <answer>Website Redesign</answer>
   </qa_pair>
   <qa_pair>
      <question>Search for issues labeled as "bug" that were closed in March 2024. Which user closed the most issues? Provide their username.</question>
      <answer>sarah_dev</answer>
   </qa_pair>
</evaluation>

Running Evaluations

The evaluation script (scripts/evaluation.py) supports three transport types:

Important:

stdio transport: The evaluation script automatically launches and manages the MCP server process for you. Do not run the server manually.
sse/http transports: You must start the MCP server separately before running the evaluation. The script connects to the already-running server at the specified URL.

1. Local STDIO Server

For locally-run MCP servers (script launches the server automatically):

python scripts/evaluation.py \
  -t stdio \
  -c python \
  -a my_mcp_server.py \
  evaluation.xml

With environment variables:

python scripts/evaluation.py \
  -t stdio \
  -c python \
  -a my_mcp_server.py \
  -e API_KEY=abc123 \
  -e DEBUG=true \
  evaluation.xml

2. Server-Sent Events (SSE)

For SSE-based MCP servers (you must start the server first):

python scripts/evaluation.py \
  -t sse \
  -u https://example.com/mcp \
  -H "Authorization: Bearer token123" \
  -H "X-Custom-Header: value" \
  evaluation.xml

3. HTTP (Streamable HTTP)

For HTTP-based MCP servers (you must start the server first):

python scripts/evaluation.py \
  -t http \
  -u https://example.com/mcp \
  -H "Authorization: Bearer token123" \
  evaluation.xml

Command-Line Options

usage: evaluation.py [-h] [-t {stdio,sse,http}] [-m MODEL] [-c COMMAND]
                     [-a ARGS [ARGS ...]] [-e ENV [ENV ...]] [-u URL]
                     [-H HEADERS [HEADERS ...]] [-o OUTPUT]
                     eval_file

positional arguments:
  eval_file             Path to evaluation XML file

optional arguments:
  -h, --help            Show help message
  -t, --transport       Transport type: stdio, sse, or http (default: stdio)
  -m, --model           Claude model to use (default: claude-3-7-sonnet-20250219)
  -o, --output          Output file for report (default: print to stdout)

stdio options:
  -c, --command         Command to run MCP server (e.g., python, node)
  -a, --args            Arguments for the command (e.g., server.py)
  -e, --env             Environment variables in KEY=VALUE format

sse/http options:
  -u, --url             MCP server URL
  -H, --header          HTTP headers in 'Key: Value' format

Output

The evaluation script generates a detailed report including:

Summary Statistics:
Accuracy (correct/total)
Average task duration
Average tool calls per task
Total tool calls

Per-Task Results:
Prompt and expected response
Actual response from the agent
Whether the answer was correct (✅/❌)
Duration and tool call details
Agent's summary of its approach
Agent's feedback on the tools

Save Report to File

python scripts/evaluation.py \
  -t stdio \
  -c python \
  -a my_server.py \
  -o evaluation_report.md \
  evaluation.xml

Complete Example Workflow

Here's a complete example of creating and running an evaluation:

Create your evaluation file (my_evaluation.xml):

<evaluation>
   <qa_pair>
      <question>Find the user who created the most issues in January 2024. What is their username?</question>
      <answer>alice_developer</answer>
   </qa_pair>
   <qa_pair>
      <question>Among all pull requests merged in Q1 2024, which repository had the highest number? Provide the repository name.</question>
      <answer>backend-api</answer>
   </qa_pair>
   <qa_pair>
      <question>Find the project that was completed in December 2023 and had the longest duration from start to finish. How many days did it take?</question>
      <answer>127</answer>
   </qa_pair>
</evaluation>

Install dependencies:

pip install -r scripts/requirements.txt
export ANTHROPIC_API_KEY=your_api_key

Run evaluation:

python scripts/evaluation.py \
  -t stdio \
  -c python \
  -a github_mcp_server.py \
  -e GITHUB_TOKEN=ghp_xxx \
  -o github_eval_report.md \
  my_evaluation.xml

Review the report in github_eval_report.md to:

See which questions passed/failed
Read the agent's feedback on your tools
Identify areas for improvement
Iterate on your MCP server design

Troubleshooting

Connection Errors

If you get connection errors:

STDIO: Verify the command and arguments are correct
SSE/HTTP: Check the URL is accessible and headers are correct
Ensure any required API keys are set in environment variables or headers

Low Accuracy

If many evaluations fail:

Review the agent's feedback for each task
Check if tool descriptions are clear and comprehensive
Verify input parameters are well-documented
Consider whether tools return too much or too little data
Ensure error messages are actionable

Timeout Issues

If tasks are timing out:

Use a more capable model (e.g., claude-3-7-sonnet-20250219)
Check if tools are returning too much data
Verify pagination is working correctly
Consider simplifying complex questions