Skip to main content
Glama
basemouse

BaseMouse

by basemouse
OverviewSchemaRelated ServersScoreDiscussions
JavaScript

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool serves a clearly distinct purpose: search for retrieval, get_context_pack for structured output, and upsert_document for writing. No overlap or ambiguity.

    Naming Consistency5/5

    All tools follow a consistent verb_noun (snake_case) pattern: search, get_context_pack, upsert_document. Naming is predictable and clear.

    Tool Count4/5

    With 3 tools, the set is minimal but covers core operations. It feels slightly under-scoped, but still reasonable for a focused workspace assistant.

    Completeness2/5

    Missing obvious operations like delete, list, or direct get_document. The domain of document management requires these for full CRUD; agents would struggle without deletion or listing.

  • Average 3.8/5 across 3 of 3 tools scored. Lowest: 3.1/5.

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

    • No community issues in the last 6 months
    • 4 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.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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

  • This repository includes a glama.json configuration file.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?

    No annotations provided, so description full burden for behavioral cues. Adds quota consumption and mentions cited/checksummed output, but does not state read-only nature or side effects beyond quota. Some context but not comprehensive.

    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?

    Two concise sentences: first defines tool output, second adds quota info. Slightly dense jargon ('basemouse.context_pack.v1') but efficient overall.

    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?

    No output schema provided, yet description only vaguely describes output as 'grounded JSON with provenance' without specifics. Lacks details on return structure, error cases, or authentication needs beyond quota mention. Incomplete for a 5-parameter tool.

    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 has 100% coverage with descriptions for all 5 parameters. Description does not add any additional parameter meaning or interaction context beyond schema. Baseline 3 is appropriate.

    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?

    Description clearly states it generates a 'cited, checksummed' context pack JSON with provenance. While it does not explicitly distinguish from siblings 'search' and 'upsert_document', the unique purpose of generating a pack is evident.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    No guidance on when to use this tool vs alternatives. The only usage note is about quota consumption, which is a constraint rather than a decision criterion.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior3/5

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

    No annotations are provided, so the description carries full burden. It discloses the search scope, ranking, and match terms, plus retrieval modes (lexical default, hybrid adds graph+vector signals). However, it does not discuss any behavioral traits like rate limits, permissions, or data freshness. Adequate but not thorough.

    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 two sentences, front-loaded with the core purpose, and contains no filler. Every sentence adds value.

    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 4 parameters, no output schema, and sibling tools, the description covers the main aspects: search scope, ranking, and modes. It could mention default result count or pagination, but the description is sufficiently complete for a search tool.

    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 parameters are already documented. The description adds value by explaining the default retrieval mode (lexical) and what hybrid mode adds ('graph + local vector signals'). This context helps the agent choose between modes.

    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: 'Search the BaseMouse repository...' and specifies the output: 'Returns ranked lexical matches with scores and matched terms.' It distinguishes from sibling tools (get_context_pack, upsert_document) which serve different purposes.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description does not explicitly state when to use this tool versus alternatives or when not to use it. While the purpose is clear, no guidance is given on exclusions or context (e.g., when to prefer hybrid mode). Usage is implied but not detailed.

    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 full burden. It discloses idempotency, revision history, tag merging behavior, and auth requirements. It does not cover error states or response format, but covers key behavioral traits well.

    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?

    Three sentences, no fluff, front-loaded with purpose. Every sentence adds value.

    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 6 parameters and no output schema, the description covers behavioral aspects (idempotency, tag merging, auth) quite well. It lacks return value details but that is partly excused by lacking output schema.

    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 100%, so the schema already documents parameters. The description adds general context (tags merge additively) but does not improve per-parameter 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 'Create or update a document' as the verb+resource, and positions it as 'the write half of agent memory', which distinctly contrasts with the sibling tools 'search' and 'get_context_pack'.

    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 explicitly notes this is for write operations and mentions auth requirements. While it doesn't directly say when not to use it, the context of it being the write half versus read siblings provides clear usage guidance.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

basemouse-core MCP server

Copy to your README.md:

Score Badge

basemouse-core MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/basemouse/basemouse-core'

If you have feedback or need assistance with the MCP directory API, please join our Discord server