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matlock08

Watson Discovery MCP Server

by matlock08
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.10

  • Disambiguation5/5

    Each tool has a distinct purpose: get_projects lists projects, list_project_collections lists collections for a project, and query_project retrieves documents. There is no overlap or ambiguity.

    Naming Consistency5/5

    All tools use snake_case with a consistent verb_noun pattern (get_projects, list_project_collections, query_project). No mixing of styles.

    Tool Count3/5

    With only 3 tools, the server is quite minimal for a service like Watson Discovery, which typically involves many more operations. However, it provides a basic read-only workflow, so it is borderline acceptable.

    Completeness2/5

    The server only supports read operations (list projects, list collections, query). Missing create, update, delete for projects and collections, as well as document management features, leaving significant gaps for agents.

  • Average 4/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 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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  • 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

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    Describes behavior as reading collections and returning structured info. No annotations provided, so description carries full burden, but lacks details on pagination, errors, or performance. Adequate for a read list operation.

    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?

    Well-structured with sections (Description, Function, Use Cases, Authentication, Output Format). Some redundancy ('return a lists' vs 'listing'), but overall efficient and front-loaded.

    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?

    Covers authentication, use cases, and output format details (name and collection_id). Missing error handling and pagination, but for a simple list tool this is reasonable.

    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 coverage is 0% – no parameter-level description. The description only implies project_id via 'specified project', adding minimal meaning beyond the schema's title. Fails to compensate for low coverage.

    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?

    Clearly states 'list existing collections for the specified project' – a specific verb+resource. Differentiates from siblings (get_projects lists projects, query_project queries data).

    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?

    Provides explicit use cases (inventory, selection, pre-processing) and mentions authentication requirements. Lacks explicit 'when not to use' or comparison to siblings, but context is clear.

    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?

    No annotations are provided, so the description carries the full burden. It discloses authentication requirements (IBM Cloud IAM credentials) and output format (structured array with document_id, metadata, passages). It does not mention side effects, but as a read operation this is acceptable.

    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 distinct sections (Description, Function, Use Cases, Authentication, Output Format). All sentences are relevant and front-loaded with purpose. It is slightly verbose but not excessive.

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

    Completeness3/5

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

    Given 5 parameters, no output schema, and no sibling context issues, the description covers authentication and output format but lacks parameter details. Use cases are provided. It is partially complete but missing crucial per-parameter guidance.

    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 individual parameters like project_id, collection_id (type array), count, or filter. The mention of natural language queries only hints at the natural_language_query parameter. The description does not compensate for the lack of schema documentation.

    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 that the tool searches data using natural language queries for a specified project and collections, returning matching documents. It also mentions connection to IBM Watson Discovery, distinguishing it from sibling tools like get_projects and list_project_collections which are for listing.

    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 'Use Cases' section provides two clear examples (searching documents, integration with workflows). It does not explicitly state when not to use or name alternatives, but the sibling tools imply the scope is for document search within a project, providing good context.

    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?

    Without annotations, the description covers behavioral aspects like authentication needs and output structure. It explicitly states the function is to list projects, implying no destructive side effects, though it could state 'read-only' more directly.

    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 sections and front-loaded main purpose. It is slightly verbose but every section contributes useful context without excessive repetition.

    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 no output schema, the description fully explains the return structure (name, project_id, type, collection_count) and authentication. It is complete for a list operation with no parameters.

    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 tool has zero parameters and 100% schema coverage, so description adds value beyond schema by detailing output fields and authentication. Baseline of 4 is appropriate.

    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 it retrieves a list of projects with human-readable names and UUIDs. It distinguishes from sibling tools like 'list_project_collections' and 'query_project' by focusing exclusively on projects.

    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 lists concrete use cases and mentions authentication requirements, implying when to use it (e.g., inventory, pre-processing). It doesn't explicitly contrast with alternatives, but the sibling tools are sufficiently different to avoid confusion.

    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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