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AnirudhB-6001

Chronicle Beta

by AnirudhB-6001
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Python
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Server Quality Checklist

67%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools are completely distinct: retrieve_chunks handles data retrieval while health_check verifies connectivity/status. There is no overlap or ambiguity between their purposes.

    Naming Consistency5/5

    Both tools follow a clear verb_noun pattern using snake_case (retrieve_chunks, health_check). The naming convention is consistent and predictable.

    Tool Count3/5

    With only 2 tools, the set feels thin for a full-featured retrieval server, though it covers the core retrieval use case plus a health check. It is borderline but could be expanded.

    Completeness4/5

    The retrieval operation is well-covered with filtering and date windows, and health_check provides necessary diagnostics. However, there are no tools for browsing available filters, projects, or content types, which agents might need.

  • Average 4.4/5 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 AGPL 3.0.

  • This repository includes a README.md file.

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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, the description carries the full burden. It discloses returns (server info, environment flags) and behavior (runs test retrieval to verify accessibility). The phrase 'light probe' implies non-destructive, though read-only is not explicitly stated.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    Single sentence effectively conveys purpose, output, and verification behavior without redundancy. Every phrase earns its place.

    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 simple tool with one optional parameter and no output schema, the description covers what it does, what it returns, and how it verifies. It omits return format details, but these are not critical for a health check.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema coverage is 100% for the single parameter (probe_query). The description's mention of 'tiny test retrieval' complements the schema by linking the parameter's purpose, but adds minimal extra meaning beyond what the schema already 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?

    Description states 'Light connectivity and status probe' with clear verb 'probe' and resource (server/vector store). It distinguishes from sibling retrieve_chunks by framing as a connectivity test rather than actual retrieval.

    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?

    Clear context implies use for checking health/connectivity, reinforced by 'tiny test retrieval' suggesting it is not for real queries. Does not explicitly name alternatives or exclusions, but context alone is sufficient for a simple probe tool.

    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, the description carries the full burden. It thoroughly explains the behavior: it accepts one or more queries, returns structured chunks with listed fields, and instructs the LLM to synthesize evidence-based answers. It doesn't explicitly state read-only behavior or rate limits, but 'retrieve' implies it, and the return format and no-side-effect expectation are clear.

    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 a clear opening sentence, a bulleted list of best practices, and a final paragraph about return fields and follow-up synthesis. It is somewhat lengthy but every section earns its place by providing practical guidance. The bullet points improve scanability.

    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 5 parameters, nested objects, and no output schema, the description covers all key aspects: what the tool does, how to structure queries, when to use filters, and what fields are returned. It also provides downstream instructions for the calling LLM. It doesn't mention error handling or pagination, but these are not essential for the tool's core purpose, so the completeness is high.

    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 already provides 100% coverage of parameter descriptions. The description adds meaningful guidance beyond the schema, such as recommending 3-5 keyword-dense query variations, using array syntax instead of full sentences, and explaining how filters and date parameters should be used. This elevates the semantic clarity beyond the baseline.

    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 runs Chronicle retrieval for a string or list of sub-queries and returns standardized chunks with metadata. It mentions optional date window and metadata filters, and the first sentence is specific and action-oriented. The sibling 'health_check' is clearly unrelated, so this tool is well-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 includes a dedicated 'Best practices for the calling LLM' section with explicit guidance on decomposing complex questions, using array syntax, applying filters, and using date filters. It tells the agent exactly when and how to use the tool, effectively substituting for absence of sibling alternatives.

    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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  • Evaluate tool definition quality.

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