CircleCI MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation3/5
Most tools have distinct purposes, but there is significant overlap between run_pipeline, run_evaluation_tests, and rerun_workflow, which all involve triggering or re-running CI pipelines. Additionally, analyze_diff and config_helper are somewhat ambiguous as they analyze code/config but aren't clearly part of the core CircleCI domain. An agent might struggle to choose between these overlapping tools.
Naming Consistency4/5
The majority of tools follow a consistent verb_noun pattern (e.g., list_followed_projects, get_build_failure_logs, run_pipeline). However, there are minor deviations like analyze_diff (verb_noun but not clearly CircleCI-specific) and config_helper (noun_verb pattern), which slightly break the consistency. Overall, naming is mostly predictable and readable.
Tool Count4/5
With 13 tools, the count is reasonable for a CircleCI server, covering monitoring, debugging, and pipeline operations. However, it feels slightly heavy due to redundant pipeline-running tools and some tools (like create_prompt_template) that seem out of scope for a CI/CD server, suggesting the set could be more tightly scoped.
Completeness3/5
The server covers core CircleCI operations like listing projects, checking status, getting logs, and running pipelines, but there are notable gaps. For example, there are no tools for managing project settings, webhooks, or artifacts, and tools like create_prompt_template and recommend_prompt_template_tests seem unrelated to CircleCI, creating confusion about the domain. This incompleteness may hinder agent workflows.
Average 4.3/5 across 13 of 13 tools scored. Lowest: 3.5/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool 'generates an array of recommended tests' and specifies the output as a 'structured array of test cases,' which adds some behavioral context. However, it doesn't cover important aspects like whether this is a read-only operation, potential side effects, error handling, or performance considerations. The description provides basic behavior but lacks depth for a tool with no annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections like 'About this tool,' 'Parameters,' 'Example usage,' and 'Tool output instructions,' making it easy to scan. It's appropriately sized with no redundant information, though the example usage could be more concise. Every sentence adds value, and it's front-loaded with the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (1 parameter with nested objects, no output schema, and no annotations), the description is moderately complete. It explains the purpose, parameters, and output format, but lacks details on behavioral traits, error cases, or integration with sibling tools. Without annotations or output schema, the description should do more to cover all aspects, but it provides a baseline understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description lists and briefly explains all parameters (promptTemplate, contextSchema, promptOrigin, model) in a 'Parameters' section, adding meaning beyond the input schema. Since schema description coverage is 0% (based on context signals), the description compensates well by documenting the parameters. However, it doesn't fully explain the semantics of 'contextSchema' or 'promptOrigin' in detail, and there's a discrepancy: the description lists 'params' as an object with specific properties, but the schema shows 'params' with different property names (e.g., 'template' vs. 'promptTemplate').
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'generates an array of recommended tests for a given prompt template.' It specifies the verb ('generates') and resource ('recommended tests'), though it doesn't explicitly differentiate from sibling tools like 'run_evaluation_tests' or 'find_flaky_tests' which might have overlapping testing functions. The description is specific but lacks sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides some context by mentioning it's 'part of a toolchain that generates and provides test cases for a prompt template,' implying usage in a testing workflow. However, it doesn't explicitly state when to use this tool versus alternatives like 'run_evaluation_tests' or 'create_prompt_template,' nor does it specify prerequisites or exclusions. The guidance is implied but not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses key behavioral traits: it analyzes/validates/fixes configs, returns errors for invalid configs with the original config, and does nothing for valid configs. It also notes the configFile should be a properly escaped string. This covers the core behavior well, though it doesn't mention rate limits, authentication needs, or detailed error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is structured with sections (purpose, parameters, example, notes, output instructions), but it's somewhat verbose with redundant details (e.g., repeating configFile info). Sentences like 'The configFile content should be provided as a properly escaped string with newlines represented as \n.' could be more concise. It's front-loaded with purpose, but some parts feel unnecessary.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and 1 parameter with 0% schema coverage, the description does a good job of completeness. It explains the tool's purpose, parameter usage, example, and output behavior. However, it lacks details on error formats, what 'fix' entails (e.g., automatic corrections vs. suggestions), and integration with sibling tools, leaving some gaps for a config validation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds significant meaning: it explains that configFile is 'the full contents of the CircleCI config file as a string' and 'should be the raw YAML content, not a file path,' with an example and note about escaping. This clarifies the parameter's purpose and format beyond the bare schema, though it doesn't detail all possible input constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'analyze and validate and fix CircleCI configuration files.' It specifies the resource (CircleCI config files) and the actions (analyze, validate, fix). However, it doesn't explicitly differentiate from sibling tools like 'run_pipeline' or 'rerun_workflow' which might also interact with CircleCI configurations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through the example and output instructions, suggesting it's for validating/fixing configs before execution. However, it doesn't explicitly state when to use this vs. alternatives like 'run_pipeline' (which might validate as part of execution) or provide clear exclusions. The guidance is present but not explicit about alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It describes the analysis process and default behavior, but lacks critical behavioral details: it doesn't specify if this is a read-only or mutating operation, what permissions or prerequisites are needed, how errors are handled, or any rate limits. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded: it starts with the core purpose, then details parameters and returns. Every sentence adds value, such as explaining defaults and parameter semantics. It could be slightly more concise by integrating the returns section more seamlessly, but overall it's efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (analyzing diffs with rules) and lack of annotations and output schema, the description is moderately complete. It covers the purpose, parameters, and returns, but misses behavioral aspects like error handling or side effects. Without an output schema, it briefly mentions the return type ('list of rule violations'), but more detail on the output structure would help. It's adequate but has clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds substantial meaning beyond the input schema. The schema has 0% description coverage for its single parameter ('params'), but the description explains the nested parameters (speedMode, filterBy, diff, rules) in detail, including their purposes, defaults, and usage notes (e.g., diff defaults to staged changes, rules location and combination method). This compensates well for the schema's lack of descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'analyze a git diff against IDE rules to identify rule violations.' It specifies the verb ('analyze'), resource ('git diff'), and target ('IDE rules'), distinguishing it from sibling tools focused on pipelines, tests, or configurations. This is specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool: for analyzing git diffs (unstaged, staged, or all changes) against IDE rules. It mentions the default behavior (staged changes) and user overrides. However, it does not explicitly state when NOT to use it or name alternatives among siblings, though the sibling list suggests no direct alternatives for this function.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses important behavioral traits: the tool requires post-processing ('MUST analyze the flaky test data and implement appropriate fixes'), has critical truncation handling requirements with specific output format instructions, and enforces strict parameter validation rules. However, it doesn't mention rate limits, authentication needs, or what happens when no flaky tests are found.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is excessively long (over 400 words) with redundant formatting. While the information is valuable, it includes instructional content that belongs in usage guidelines rather than pure description. The structure with all-caps headings and excessive bullet points makes it harder to scan quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (multiple parameter options, no annotations, no output schema), the description does an excellent job covering parameter usage and behavioral requirements. It explains what the tool does, how to use it, and what to do with the output. The main gap is lack of information about the output format or structure of the returned flaky test data.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must fully compensate. It provides comprehensive parameter semantics: explains the three mutually exclusive parameter options, gives specific examples for each parameter, clarifies relationships between parameters, and provides detailed format requirements. This goes far beyond what the bare schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool 'retrieves information about flaky tests in a CircleCI project', which is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'get_job_test_results' or 'get_build_failure_logs', which might also provide test-related information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides extensive usage guidance with explicit requirements: 'EXACTLY ONE of these THREE options must be used', detailed parameter requirements for each option, and clear instructions about what to do when parameters are incomplete. It also mentions the 'listFollowedProjects' tool as a source for projectSlug values.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries the full burden. It discloses important behavioral traits: the tool requires exactly one of three parameter options, has strict input validation ('Never call this tool with incomplete parameters'), and includes error handling guidance ('ask the user for the missing information'). However, it doesn't mention rate limits, authentication needs, or what happens if multiple pipelines exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose, use cases, input options, workflow, requirements), but it's verbose with repetitive content. For example, the 'Common use cases' section lists 5 items that essentially restate the purpose. Some sentences could be more concise while maintaining clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (multiple parameter options, no annotations, no output schema), the description is mostly complete. It thoroughly explains inputs, usage, and constraints. However, it doesn't describe the return value format or potential error responses, which would be helpful since there's no output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the schema provides no parameter descriptions. The description compensates fully by explaining all 5 parameters in detail, including their purposes, formats, and interdependencies (e.g., 'EXACTLY ONE of these THREE options must be used'). It provides concrete examples and clarifies which parameters must be used together.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool 'retrieves the status of the latest pipeline for a CircleCI project' with specific verbs ('retrieves', 'check', 'get', 'view') and resource ('pipeline'). However, it doesn't explicitly differentiate from sibling tools like 'get_build_failure_logs' or 'get_job_test_results', which might also retrieve pipeline-related information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool, including a 'Recommended Workflow' section that references the sibling tool 'listFollowedProjects' and step-by-step instructions. It also includes 'Additional Requirements' that specify when not to use the tool (e.g., with incomplete parameters) and alternatives (e.g., asking the user for missing information).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes what the tool does (generates structured prompt templates and context schemas), what it returns, and how the output connects to other tools (specifically 'recommend_prompt_template_tests'). It explains the tool's role in a broader workflow and provides concrete examples. The only minor gap is lack of explicit mention about error handling or performance characteristics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (ABOUT THIS TOOL, WHEN SHOULD THIS TOOL BE TRIGGERED, PARAMETERS, etc.), but it's quite lengthy with some redundancy. While most content is valuable, some information (like the detailed examples) could potentially be streamlined. It's front-loaded with purpose information, but the overall length reduces conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of the tool (generating structured templates from varied inputs) and the absence of both annotations and output schema, the description does an excellent job of explaining what the tool does, when to use it, what parameters mean, and what outputs to expect. It even connects to sibling tools. The main gap is the lack of explicit output schema documentation, though the description does outline what the tool returns.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must fully compensate. It provides detailed parameter information in the 'PARAMETERS' section, explaining each parameter's purpose, possible values, and defaults. The examples further clarify usage. This adds substantial value beyond the bare schema, though it doesn't explicitly map parameters to the two usage scenarios mentioned earlier.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'generate a prompt template based on feature requirements or pre-existing prompts.' It specifies the verb ('generate'), resource ('prompt template'), and distinguishes between two distinct input scenarios. This is specific and comprehensive, going beyond a simple restatement of the tool name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit, detailed guidance on when to trigger the tool: for new AI application/feature requirements or for testing/evaluating/modifying pre-existing prompts. It also includes important exclusions, stating that similar files should 'NEVER be generated directly by the AI agent' and must use this tool instead. This offers clear alternatives and boundaries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the optional 'fromFailed' parameter's default behavior ('If omitted, behavior is based on workflow status'), which is valuable context. However, it doesn't mention authentication requirements, rate limits, side effects, or what happens to the original workflow instance, leaving gaps for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (purpose, use cases, input options) and uses bullet points effectively. While slightly verbose, every sentence serves a purpose - the URL format examples are particularly helpful. The information is front-loaded with the core purpose stated first.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations and no output schema, the description does a good job covering the essential aspects: purpose, usage scenarios, parameter semantics, and input constraints. The main gaps are the lack of information about authentication, side effects, and return values, but given the comprehensive parameter coverage and clear usage guidance, it's mostly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage (the schema has descriptions but context signals indicate 0% coverage), the description provides comprehensive parameter information. It explains the two input options, clarifies the exclusive choice requirement, documents the optional 'fromFailed' parameter with its default behavior, and provides URL format examples with placeholders, adding significant value beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('rerun a workflow') and specifies the scope ('from start or from the failed job'). It distinguishes this from sibling tools like 'run_pipeline' or 'run_rollback_pipeline' by focusing specifically on re-executing existing workflows rather than initiating new ones.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly provides usage guidance through 'Common use cases' listing both scenarios (rerun from failed job or from start). It also provides clear parameter selection rules ('EXACTLY ONE of these TWO options must be used'), which helps the agent choose between workflowId and workflowURL approaches.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes critical behavioral traits: truncation handling (checking for <MCPTruncationWarning>, required warning message), test result filtering logic (how to use filterByTestsResult parameter), and strict parameter requirements (exactly one of three options, never call with incomplete parameters). However, it doesn't cover rate limits, authentication needs, or error handling scenarios.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (PRIORITY USE CASE, CRITICAL REQUIREMENTS, Input options), but it's overly verbose with repetitive information (e.g., 'Get test metadata for...' listed six times). Some sentences could be more concise, and the 'Common use cases' section adds little value beyond what's already implied.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 6 parameters, no annotations, and no output schema, the description provides substantial context: purpose, usage guidelines, behavioral requirements, and detailed parameter semantics. It effectively guides the agent on how to invoke the tool correctly. The main gap is the lack of output format information, which would be helpful given the complexity of test metadata.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Given 0% schema description coverage (schema has no descriptions for properties), the description fully compensates by providing extensive parameter semantics. It explains all six parameters through three clear options, defines their relationships (e.g., 'Option 1 - Project Slug and branch (BOTH required)'), and adds practical guidance like 'Never call this tool with incomplete parameters' and format examples for URLs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verb ('retrieves') and resource ('test metadata for a CircleCI job'), distinguishing it from siblings like 'get_latest_pipeline_status' (status vs. metadata) and 'get_build_failure_logs' (logs vs. metadata). It explicitly identifies what the tool does beyond just the name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool (e.g., 'When asked "are tests passing in CI?" or similar questions about test status') and includes priority use cases. It also distinguishes when to use specific parameter options (e.g., 'For simple test status checks... prefer Option 1 with a recent pipeline URL if available'), offering clear alternatives within the tool itself.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it returns a list of projects with name and projectSlug, includes pagination limits ('If pagination limits are reached...'), and specifies workflow constraints (e.g., waiting for user instruction). It does not cover error handling or authentication needs, but provides substantial context beyond basic functionality.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (Common use cases, Returns, Workflow, Note, IMPORTANT), making it easy to scan. It is slightly verbose in the 'IMPORTANT' section with repetitive warnings, but overall, each sentence adds value, such as clarifying the tool's role in workflows and preventing misuse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (simple list operation with no input parameters) and lack of annotations/output schema, the description is largely complete. It explains the purpose, usage, output format, pagination behavior, and workflow integration. It could briefly mention error cases or authentication, but it covers the essential context for an AI agent to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters, so the baseline is 4. The description does not need to explain parameters, and it appropriately focuses on output and usage instead. No additional parameter information is required or provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'lists all projects that the user is following on CircleCI.' It specifies the verb ('lists'), resource ('projects'), and scope ('following on CircleCI'), distinguishing it from sibling tools that focus on builds, pipelines, tests, or configurations rather than project listings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'Common use cases' include identifying available projects, selecting a project for operations, and obtaining projectSlug for other tools. It also includes a detailed 'Workflow' section with step-by-step instructions and an 'IMPORTANT' note on when not to use it (e.g., not automatically running additional tools without user instruction).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: the tool triggers a new pipeline (implies mutation), returns a monitoring URL, handles multiple pipeline definitions with a selection process, and has specific parameter requirements. It doesn't mention authentication needs, rate limits, or error handling, but covers the core operational behavior well.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (Input options, Configuration, Pipeline Selection, Additional Requirements, Returns) and uses bullet points effectively. While comprehensive, it's appropriately sized for a complex tool with multiple usage patterns. Some sentences in the 'Additional Requirements' section could be more concise, but overall the structure supports readability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (multiple parameter options, conditional logic, no annotations, no output schema), the description provides substantial context. It explains the return value (monitoring URL), documents the pipeline selection workflow, and provides usage constraints. It doesn't cover error cases or response formats in detail, but addresses the core functionality adequately for the complexity level.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by providing comprehensive parameter semantics. It explains the three distinct usage options with detailed examples, clarifies mutual exclusivity requirements, documents the pipeline selection logic, and provides concrete formatting examples for URLs and paths. This adds substantial value beyond what the bare schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('triggers a new CircleCI pipeline') and the resource ('CircleCI pipeline'), distinguishing it from siblings like 'get_latest_pipeline_status' (monitoring) or 'rerun_workflow' (re-running). The opening sentence provides a complete, unambiguous purpose statement.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool versus alternatives through detailed parameter requirements and constraints. It specifies that 'EXACTLY ONE of these THREE options must be used' and includes specific rules for each option, plus guidance on pipeline selection scenarios. The 'Additional Requirements' section further clarifies usage boundaries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and excels by disclosing critical behavioral traits: it describes the interactive, multi-step flow with user prompts; specifies error handling (e.g., definitive error messages without suggesting alternative projects); outlines constraints (e.g., never guess project slugs, do not call with incomplete parameters); and details success/error returns. This goes far beyond basic functionality.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections (Initial Step, Typical Flow, Parameters, Behavior, Returns, Important Note), but it is overly verbose with repetitive points (e.g., multiple warnings about not suggesting alternative projects). Some sentences could be condensed without losing clarity, making it longer than necessary for effective tool selection.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (multi-step interactive tool), no annotations, no output schema, and 0% schema coverage, the description is highly complete. It thoroughly explains the process, parameters, behavior, returns, and constraints, leaving no gaps for an AI agent to understand how to invoke it correctly in context with sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It lists all parameters with brief explanations (e.g., 'projectSlug or projectID must be provided', 'optional at first if multiple environments'), clarifies dependencies (e.g., 'branch must also be provided' for projectSlug), and ties parameters to the flow (e.g., 'rollback_type set to WORKFLOW_RERUN'). While it adds significant value, some param details like format specifics are light, preventing a perfect score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'Run a rollback pipeline for a CircleCI project' with specific verbs ('guides', 'adapting', 'prompting') and distinguishes it from siblings like 'rerun_workflow' and 'run_pipeline' by focusing on the full rollback process with user interaction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit when-to-use guidance: it instructs to first call 'listFollowedProjects' for project selection, details the typical flow with conditional steps (e.g., if multiple components/environments), and specifies when not to use it (e.g., if project lacks rollback configuration, do not suggest alternatives). It clearly differentiates from siblings by outlining a guided, interactive process.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It thoroughly explains critical behavioral traits, such as truncation handling with specific warning requirements, input constraints (mutually exclusive options), and prerequisites like obtaining project slugs from another tool. This goes beyond what a minimal description would cover, ensuring the agent understands operational nuances.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (e.g., 'CRITICAL REQUIREMENTS', 'Input options', 'Recommended Workflow'), but it is lengthy due to the complexity of the tool. While every sentence serves a purpose, such as detailing truncation handling and parameter rules, it could be more front-loaded; the core purpose is stated early, but some critical details are buried in later sections.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (multiple input options, critical behavioral requirements) and lack of annotations or output schema, the description is highly complete. It covers purpose, usage guidelines, behavioral traits, parameter semantics, and error handling (e.g., truncation warnings), leaving no significant gaps for the agent to operate effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate fully. It does so by detailing all parameters within three structured options, explaining their relationships (e.g., 'projectSlug and branch (BOTH required)'), providing examples, and clarifying dependencies. This adds significant meaning beyond the bare schema, ensuring the agent understands how to use the parameters correctly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'retrieving failure logs' to 'debug CircleCI build failures.' It specifies the exact resource (failure logs) and verb (retrieve), and distinguishes it from siblings like 'get_job_test_results' or 'get_latest_pipeline_status' by focusing specifically on failure logs rather than test results or status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool versus alternatives, including a 'Recommended Workflow' that references the sibling tool 'listFollowedProjects' for obtaining project slugs. It also details three distinct input options with clear conditions (e.g., 'EXACTLY ONE of these THREE options must be used') and warns against incomplete parameters, offering comprehensive usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It thoroughly describes the tool's behavior: it triggers a new CircleCI pipeline, generates a temporary configuration file, returns a project slug or URL, handles multiple pipeline definitions with a selection process, and includes constraints like requiring exactly one of three input options and not constructing URLs. This covers operational details, constraints, and output behavior comprehensively.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and well-structured, with clear sections (e.g., Input options, Test Files, Pipeline Selection, Additional Requirements, Returns). It is front-loaded with the core purpose and key behavior. While comprehensive, some sentences could be more concise (e.g., the detailed URL formats in Option 2), but overall, it efficiently conveys necessary information without waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (multiple input options, nested objects, no output schema, and no annotations), the description is complete. It covers all aspects: purpose, usage guidelines, behavioral traits, parameter semantics, and return values (URL to monitor progress). It addresses potential edge cases like multiple pipeline definitions and incomplete parameters, ensuring the agent has sufficient context to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage (based on context signals indicating 0% schema description coverage), so the description must compensate. It adds extensive meaning beyond the schema by explaining the three input options in detail, specifying required parameters for each option, describing 'promptFiles' as array objects from the ./prompts directory, and clarifying 'pipelineChoiceName' usage for multiple pipelines. This fully compensates for the lack of schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'run evaluation tests on a circleci pipeline' and 'triggers a new CircleCI pipeline and returns the URL to monitor its progress.' It distinguishes from siblings like 'run_pipeline' by specifying it's for evaluation tests (referred to as 'Prompt Tests' or 'Evaluation Tests'), making the purpose specific and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidelines, including when to use this tool (for running evaluation tests on CircleCI pipelines) and detailed conditions for three input options. It also specifies prerequisites like 'Never call this tool with incomplete parameters' and references sibling tools (e.g., 'listFollowedProjects'), offering clear alternatives and exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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