Skip to main content
Glama
cquiroz6211

Cerebro de compañía

by cquiroz6211
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Local

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation4/5

    Each tool targets a distinct action: querying the brain, saving meetings, saving chat summaries, and saving general notes. Some potential overlap between save_chat_summary and save_note, but descriptions clarify the intended use.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern (ask_brain, ingest_meeting, save_chat_summary, save_note) using snake_case, making the naming predictable and clear.

    Tool Count5/5

    Four tools cover the core operations for a company knowledge brain: querying and saving different content types (meetings, chat summaries, notes). The count is well-scoped for this domain.

    Completeness3/5

    The set covers adding knowledge and querying (though ask_brain is a placeholder), but lacks update or delete operations. For a knowledge base, this leaves notable gaps in lifecycle management.

  • Average 2.5/5 across 4 of 4 tools scored.

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

    • No community issues in the last 6 months
    • 11 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

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • 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

  • Behavior2/5

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

    No annotations exist, so the description must disclose behavioral traits. It minimally states it queries local Markdown knowledge but omits details on return format, pagination, error handling, or what happens if the query fails. The placeholder note undermines trust in its current behavior.

    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 short with two sentences. The first sentence is concise and front-loaded, but the second sentence (placeholder note) is not directly useful for tool selection, slightly reducing efficiency.

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

    Completeness1/5

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

    Given the lack of output schema, annotations, and parameter descriptions, the description is severely incomplete. It does not explain return values, limitations, or usage examples, leaving the agent with insufficient information to invoke the tool correctly.

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

    Parameters1/5

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

    Schema description coverage is 0%, but the description does not explain any of the 4 parameters. It mentions 'question' implicitly but provides no context for 'tags', 'limit', or 'client', leaving the agent without guidance on how to set them meaningfully.

    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 'Ask a business question over local Markdown knowledge,' specifying the verb and resource, which differentiates it from sibling tools (ingestion/saving operations). However, the 'Placeholder until GBrain retrieval is wired' caveat slightly reduces clarity as it indicates the tool is temporary.

    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 usage guidance is provided. The description does not indicate when to use this tool versus alternatives, nor does it specify any prerequisites or exclusions.

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

  • Behavior1/5

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

    No annotations provided, and the description fails to disclose any behavioral traits such as idempotency, side effects, permissions, or what happens on duplicate saves. With no annotation support, the description is insufficient.

    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?

    Single sentence is concise but lacks structure. It is front-loaded but provides no additional details or formatting, making it adequate but not optimal.

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

    Completeness1/5

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

    With 7 parameters, no output schema, and no annotations, the description is severely incomplete. It does not explain return values, behavior on required fields, or how parameters relate, leaving agents with insufficient context.

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

    Parameters1/5

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

    Schema has 0% description coverage, and the description adds no parameter information. It does not explain the meaning or usage of tags, client, source, nextActions, participants, leaving agents to guess.

    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 clearly states the action (save), resource (business conversation summary), and destination (company brain). It distinguishes from siblings like ask_brain (query), ingest_meeting (meeting), and save_note (generic).

    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 explicit guidance on when to use this tool versus siblings. Does not mention context, prerequisites, or exclusions. The description only states what it does, not when to choose it.

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

  • Behavior1/5

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

    No annotations are provided, and the description fails to disclose any behavioral traits such as whether it overwrites existing records, handles duplicates, or requires authentication. This is a significant gap for a mutation tool.

    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 sentence that is front-loaded and contains no redundant information. It earns its place by being minimal.

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

    Completeness1/5

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

    Given the tool has 8 parameters, no output schema, and no annotations, the description is far from complete. It does not explain required parameters, return behavior, or provide any usage context.

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

    Parameters1/5

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

    The input schema has 8 parameters with 0% coverage in the schema description. The description adds no meaning beyond the parameter names; for example, it does not explain 'client', 'source', 'uploadedBy', or 'meetingDate'.

    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 saves meeting records/transcripts from text or Markdown, with a specific verb and resource. It differentiates from sibling tools like ask_brain (query) and save_note (generic note).

    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 versus alternatives like save_chat_summary or save_note. The description only states what it does, not when to choose it.

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

  • Behavior2/5

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

    With no annotations, the description must disclose behavior fully. It only states the save action, omitting details on idempotency, side effects, authorization, or response format. This is insufficient for a write 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?

    The description is a single concise sentence that front-loads the action. However, it sacrifices essential detail for brevity.

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

    Completeness1/5

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

    Given no annotations, no output schema, and 6 parameters (2 required), the description is severely incomplete. It lacks information on required inputs, constraints, return values, and behavioral context.

    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%, so the description must add meaning. It lists note types aligning with the kind enum but fails to explain other parameters like body, title, tags, author, and client. Only partial semantic value for one parameter.

    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 saves various types of notes to the company brain, listing examples that match the kind enum. It implicitly differentiates from siblings like ask_brain (query) and ingest_meeting (process meeting) through the verb 'save', but does not explicitly contrast them.

    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 is provided on when to use this tool versus alternatives like ask_brain, ingest_meeting, or save_chat_summary. The description only states what the tool does, without context for selection.

    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

winterbrain MCP server

Copy to your README.md:

Score Badge

winterbrain 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/cquiroz6211/winterbrain'

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