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HiroakiKatoh

legal-impact-mapper

by HiroakiKatoh
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool serves a distinct, non-overlapping purpose: extract_fact_graph creates the graph, update_fact_classification modifies a node, and analyze_impact computes the ripple effects. An agent can clearly distinguish when to use each tool.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern (analyze_impact, extract_fact_graph, update_fact_classification) using snake_case and clear action verbs, making them predictable and easy to understand.

    Tool Count4/5

    With 3 tools, the set is minimal but covers the essential workflow for the server's purpose. Each tool earns its place, though the count is slightly low for a broader legal document analysis platform.

    Completeness3/5

    The tools cover the core workflow (create, update, analyze), but lack features like reading the graph directly, deleting nodes, or managing multiple documents. Minor gaps exist that an agent may need to work around.

  • Average 4.7/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 1 commit 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?

    No annotations exist, so description carries full burden. It discloses high cost due to LLM usage, the output structure (FactGraph with nodes/edges/confidence/group_id), and that it should be called once per document. Does not mention side effects or idempotency, but the core behavioral traits are transparent.

    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?

    Structured into sections (when to use, input, output, note). Every sentence provides essential information with no redundancy. Front-loaded with critical usage guidance.

    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 output schema, description adequately outlines output structure and interaction with siblings. Lacks details on error handling or behavior for exceeding character limit, but covers the main workflow sufficiently.

    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 already describes 'text' parameter. Description adds value by specifying max 50,000 characters and acceptable content types (contracts, legal documents, factual relations), going beyond 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 explicitly states 'extract fact graph from legal document text' and distinguishes from sibling tools by noting it is for first-time usage only, with the output passed to update_fact_classification and analyze_impact. The verb 'graph' and resource 'legal document text' are clear.

    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?

    Provides explicit when-to-use ('only when graphing for the first time'), why not to call again ('high cost from internal LLM'), and what to do with output ('pass directly to siblings'). No ambiguity about the intended workflow.

    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, the description fully explains behavior: output structure, handling of empty affected_node_ids (stop processing), risk_level=high warning, and constraints on editing scope. No contradictions.

    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?

    The description is well-structured with clear headings (usage, inputs, outputs, notes). Every sentence adds value, and it is concise despite covering many aspects.

    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 (2 required params, nested objects, no output schema), the description is very complete: it explains output structure, behavioral notes, and editing constraints. No 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?

    Schema coverage is 100%, so baseline is 3. The description adds meaning by clarifying that 'graph' must be the updated FactGraph and 'changed_node_ids' must come from update_fact_classification's output, enhancing understanding beyond 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 tool's purpose: determining the impact range after using update_fact_classification. It specifies the context and distinguishes from siblings by indicating it is used after updating classifications.

    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 explicit when-to-use (after update_fact_classification) and required inputs. While it doesn't explicitly state when not to use it, the context is clear enough for correct invocation.

    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, the description fully discloses behavior: user_verified=true blocks modifications, after a successful edit with mark_verified=true the node becomes locked, and the output structure includes changed_node_ids and diff_summary. It also explains the return value when a node is locked.

    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 (use case, input, output, notes) and is appropriately sized. It includes all necessary information without redundancy, though it could be slightly more concise.

    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?

    The description is highly complete given the tool's complexity (5 parameters, nested objects, no output schema). It covers input, output, edge cases (locked nodes), and integration with sibling tools. No gaps remain.

    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 coverage is 100%, so baseline is 3. The description adds value by listing parameters in a structured format with their purposes and defaults (e.g., mark_verified defaults to true). It also explains the output structure, which the schema does not provide.

    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 explicitly states the verb-resource ('update fact classification') and when to use it (lawyer changes node content). It distinguishes from siblings by mentioning it takes the output of extract_fact_graph and its output is fed to analyze_impact.

    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 gives explicit context for usage: 'When a lawyer changes the content of a node (text or classification)'. It also provides exclusion criteria: user_verified=true nodes reject changes, requiring setting user_verified=false first. Additionally, it instructs to pass changed_node_ids to analyze_impact.

    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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