OpenTelemetry Documentation MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
The two tools have clearly distinct purposes: 'read_documentation' fetches and converts specific documentation pages to markdown, while 'search_documentation' searches across documentation for relevant pages. There is no overlap in functionality, and an agent can easily differentiate between retrieving known content and discovering unknown content.
Naming Consistency5/5Both tools follow a consistent verb_noun pattern with snake_case naming: 'read_documentation' and 'search_documentation'. The verbs ('read' and 'search') are distinct and appropriate for their functions, and the naming scheme is predictable and readable throughout the set.
Tool Count2/5With only 2 tools, the server feels thin for its purpose of providing OpenTelemetry documentation access. While the tools cover basic retrieval and search, the scope suggests potential for more operations (e.g., listing documentation categories, filtering by version, or handling API interactions), making the count insufficient for a comprehensive documentation server.
Completeness3/5The server covers core documentation access with read and search functions, but there are notable gaps. It lacks tools for browsing documentation structure (e.g., listing sections or topics), handling updates or version-specific content, or integrating with other OpenTelemetry resources, which limits agent workflows to basic lookup tasks without broader context.
Average 4.5/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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
- 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 of behavioral disclosure. It does an excellent job describing key behavioral traits: it discloses the underlying API (Google Custom Search), usage limits (100 queries/day free tier), result limitations (10 per page), and what information each result contains. The only minor gap is it doesn't explicitly mention whether this is a read-only operation (though searching implies it is) or error handling specifics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (Usage, Search Tips, API Limits, Result Interpretation) and front-loads the core purpose. However, it could be slightly more concise - some information like the 'Returns' section is somewhat redundant with the 'Result Interpretation' section. Overall, most sentences earn their place by providing valuable guidance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that this is a search tool with 2 parameters, 100% schema coverage, and an output schema exists, the description provides excellent contextual completeness. It covers purpose, usage guidelines, behavioral traits (API limits, result format), and search optimization tips. The existence of an output schema means the description doesn't need to explain return values in detail, which it appropriately avoids.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds minimal value beyond the schema: it mentions that 'limit' will be capped at 10 due to API limitations (which is useful context), but doesn't provide additional semantic context for 'search_phrase' beyond what's in the schema. This meets the baseline expectation when schema coverage is complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search OpenTelemetry documentation using Google Custom Search' with the specific verb 'search' and resource 'OpenTelemetry documentation'. It distinguishes from the sibling tool 'read_documentation' by specifying this is for searching when you don't have a specific URL, while 'read_documentation' would presumably be for reading specific documentation pages.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does 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: 'Use it to find relevant documentation when you don't have a specific URL.' It also includes a 'Search Tips' section with detailed advice on how to formulate effective queries, and mentions API limits that inform usage decisions. The contrast with the sibling 'read_documentation' is implied through the 'when you don't have a specific URL' statement.
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 and does an excellent job disclosing behavioral traits. It explains the conversion to markdown format, handling of long documents through chunking, URL domain restrictions, output format details, and strategies for dealing with truncated responses. The only minor gap is it doesn't mention rate limits or authentication requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (Usage, URL Requirements, Example URLs, Output Format, Handling Long Documents) and every sentence adds value. It's appropriately sized for a tool with this complexity and is front-loaded with the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (document fetching, conversion, chunking), no annotations, and the existence of an output schema, the description is remarkably complete. It covers purpose, usage guidelines, behavioral details, parameter context, output format, and handling edge cases like long documents. The output schema handles return values, so the description appropriately focuses on other aspects.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the baseline is 3. The description adds some value by explaining the purpose of start_index for chunking long documents and providing example URLs, but doesn't significantly enhance parameter understanding beyond what the schema already provides about url, max_length, and start_index.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does 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 verbs ('fetch and convert') and resource ('OpenTelemetry documentation page'), distinguishing it from the sibling 'search_documentation' tool by focusing on retrieving and converting specific pages rather than searching across documentation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does 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 it (for OpenTelemetry documentation pages), when not to use it (must be from opentelemetry.io domain), and alternatives (making multiple calls with different start_index values for long documents). It also distinguishes from the sibling tool by specifying this is for reading specific pages.
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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