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rm0nroe

Catalyst Edge MCP

by rm0nroe

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v0.1.6

  • Disambiguation5/5

    Each tool targets a distinct function: one for generating/assessing research scores on a ticker, the other for retrieving source records for a specific claim ID. No overlap in purpose or inputs.

    Naming Consistency5/5

    Both tools share the 'catalyst_edge_' prefix and use snake_case with a clear noun structure. The pattern is consistent and predictable, making it easy to infer each tool's role.

    Tool Count3/5

    With only two tools, the server has a minimal but plausible scope. While each tool clearly serves a distinct purpose, the very low number makes the set feel thin and raises expectations for more coverage.

    Completeness4/5

    The two tools cover a core research workflow (get score, then read claim sources), but the server lacks other potentially useful operations such as listing dossiers or searching claims. Given the apparent narrow domain, the surface is reasonably complete for a read-only assistant, though a bit minimal.

  • Average 3.8/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 64 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing
  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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?

    Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds context about what the tool evaluates (evidence, provenance, next checks), which goes beyond the annotations and is consistent with them. 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.

    Conciseness3/5

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

    The description is one sentence, which is concise and front-loaded. However, it is under-specified relative to the tool's complexity, omitting parameter details and decision context, so it is not appropriately sized for the information an agent needs.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    With five parameters, an enum risk_mode, and no parameter descriptions in either the schema or the description, the description is incomplete. It does not explain the meaning of risk_mode values, how lookback_days affects results, or when to use this tool versus the sibling catalyst_edge_claim_sources. The existence of an output schema helps with return values but not with parameter selection.

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

    Parameters2/5

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

    Schema description coverage is 0%, and the description does not explain any of the five parameters: ticker, risk_mode (an enum), lookback_days, include_sources, or include_raw_signals. The phrase 'ticker research' implicitly relates to ticker but fails to define the semantics of the other parameters, leaving the agent without necessary guidance.

    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 is for ticker research to assess recent evidence, provenance, and next checks, which is a specific action on a specific resource. It implicitly distinguishes from the sibling tool claim_sources by focusing on assessment rather than source retrieval, though the name's 'score' and 'edge' are not explicitly mentioned.

    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 phrase 'Use for ticker research' provides a clear context for when to apply the tool. However, it does not mention when not to use it, nor does it reference the alternative sibling tool catalyst_edge_claim_sources, so exclusion and alternative guidance are missing.

    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?

    The annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds context by noting the records are 'immutable' and that results are a 'bounded page,' which clarifies pagination behavior and data characteristics. This adds value beyond the annotations without contradicting them.

    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 a single, concise sentence that front-loads the core purpose. There is no fluff or redundant information; every word 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 read-only paginated tool with an output schema, the description covers the essential usage (claim ID, bounded page). It does not explain how to navigate to subsequent pages via cursor, but that is inferable from the schema. The presence of an output schema reduces the need to describe return values.

    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 description coverage is 0%, so the description must compensate. It implicitly references claim_id via 'dossier claim ID' and hints at pagination via 'bounded page,' but it does not explicitly explain limit or cursor semantics. The schema provides names, types, defaults, and constraints, which helps, but the description adds only marginal semantic value.

    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 action ('read'), the target resource ('source records'), and the scope ('grouped claim sources'), and it requires a dossier claim ID. This distinguishes it from the sibling tool 'catalyst_edge_score', which likely performs scoring rather than reading sources.

    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?

    It gives a clear usage context by specifying it must be used with a dossier claim ID and that it returns a 'bounded page' (implying pagination). However, it does not explicitly state when to use this tool instead of alternatives or provide any exclusions, so it falls short of a 5.

    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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