jail
Server Quality Checklist
Latest release: v1.4.1
- Disambiguation5/5
The two tools serve distinct functions: search is for exploratory queries, while detail retrieves specific document metadata. There is no overlap in their use cases, making selection unambiguous.
Naming Consistency4/5Both tool names are single lowercase verbs, which is consistent, though they do not follow a more descriptive verb_noun pattern. The style is uniform and each name clearly suggests its action.
Tool Count3/5With only two tools, the server feels minimal, but the search tool is highly versatile and covers many content types. The count is borderline but reasonable for the server's focused purpose.
Completeness4/5The search and detail tools cover the core workflow of discovering and examining documents. While full-text retrieval is delegated to other tools, metadata and search are well-covered, with only minor gaps like no direct enumeration of content types.
Average 4.7/5 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
This repository is licensed under MIT License.
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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?
With no annotations provided, the description carries the burden. 'Get' implies a read-only operation, and the description doesn't suggest any side effects. It also provides example ID formats, which is helpful context. However, it doesn't explicitly state read-only or mention rate limits/authentication, so a 4 rather than 5.
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 concise: two sentences plus a well-formatted args section. It's front-loaded with the purpose and immediately clarifies usage. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with no annotations or output schema, the description covers the essential aspects: what it does, where the ID comes from, and example formats. It lacks detail on the exact contents of 'full metadata', but that's a minor gap given the tool's simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, and the description fully compensates by explaining doc_id as a Document ID from search results with concrete examples of ID formats (md5, hn, doi). This adds significant meaning beyond the schema's bare type string.
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 uses a specific verb 'Get' with a resource 'full metadata for a document', clearly distinguishing it from the sibling 'search' tool which presumably finds documents. The instruction to use IDs from search results reinforces its role as a lookup tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states to use IDs from search results, implying this tool is used after a search to retrieve details. It provides clear context but doesn't explicitly mention exclusions or alternative tools beyond the implication.
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?
No annotations were provided, so the description carries the full burden of behavioral disclosure. It effectively reveals key behavioral traits: returns ranked results but not full content, uses pagination via cursor/next_cursor, and has type-specific result sources. It also notes rate limits (Trial max 10, Pro max 50). This is rich, honest contextual disclosure beyond what a schema would convey.
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 section breaks: main purpose, usage, strategy, and args. It is front-loaded with the core statement, then detail. It is longer than a minimal two-sentence description, but nearly every sentence adds value. Minor redundancy exists (query keyword advice appears twice, once in strategy and once in args), which prevents a perfect 5.
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?
The tool has no output schema and complex behavior with multiple content types, pagination, and strategic search guidance. The description thoroughly covers what the tool does, when to use it, how to use the parameters, what the results include, and how to follow up with other tools. It is complete enough for an agent to select, invoke, and process results correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides no description coverage (0%), so the description's parameter docs are essential. It adds substantial meaning beyond the raw schema types: query gets keyword-count guidance and language advice, type gets a complete enumerated list of content types and their sources, limit gets a range and tier-specific cap, cursor is explained as an opaque pagination token tied to next_cursor. This far exceeds basic schema information.
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 opens with 'Search documents' which is a specific verb+resource pair, and immediately clarifies what it returns: 'ranked results with title, author, year, description, url, id, score — not full content.' It distinguishes itself from the sibling tool 'detail' by explicitly directing users to 'Use detail() for full metadata on promising results.' This clearly separates the search function from a detail-retrieval function.
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 an explicit 'Use when:' section that lists a broad but specific set of user intents (research, find papers/books/articles, look up facts, find discussions, legal cases) and generalizes to 'any "search for..." request.' It also gives guidance on alternatives: using fetch/browsing for full content and using detail() for metadata. This goes beyond simple usage indication and provides actionable decision rules.
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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