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DylanCodyBrown

aimm-mcp

by DylanCodyBrown
OverviewSchemaRelated ServersScoreDiscussions
Python
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a completely distinct purpose: one initializes the project, the other reads its context. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tools follow a consistent 'aimm_verb_noun' pattern (init_project, read_project_context), making naming predictable and clear.

    Tool Count2/5

    With only 2 tools, the server feels undersized for managing a data model. A more complete set (e.g., adding tables, connections) would be expected.

    Completeness2/5

    The server provides initialization and read capabilities only, lacking essential mutation tools like adding tables or connections, making it incomplete for full project management.

  • Average 4.5/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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 is failing

  • 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.

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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.

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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?

    With no annotations provided, the description carries full burden. It discloses idempotency, folder creation, and parameter roles. It does not mention permissions or side effects, but for a bootstrap tool the information is sufficient.

    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?

    Two sentences front-load the core purpose and idempotency. Every sentence adds value with no redundancy. Extremely efficient.

    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 the tool's simplicity (3 params, no output schema, single sibling), the description covers essential aspects: purpose, location, idempotency, and parameter explanations. Minor omission of return value but not critical for a bootstrap function.

    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 explaining the purpose of `name` (human-readable label for context dumps) and `description` (free-text read on every call), and notes dialect default. This enriches understanding beyond the 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 action ('Bootstrap the AIMM data model'), the target location, and the folder skeleton created. It distinguishes itself from the sibling `aimm_read_project_context` by being an initialization tool versus a read tool.

    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 states idempotency ('safe to call when already initialised'), which guides usage. However, it does not provide explicit when-not-to-use scenarios or mention alternatives beyond the implicit contrast with the sibling.

    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 full burden. It discloses return scope, cost bound, default format, and rationale. It lacks explicit statement about safety (e.g., no side effects), but the content implies read-only behavior.

    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 concise, with every sentence contributing significant information. It front-loads the main purpose, followed by usage guidance and format explanation, with no redundant words.

    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 the tool's complexity (returns many project details) and lack of output schema, the description covers what is returned, when to use it, and format options. It lacks details on error handling or size limits, but the bounded cost mention mitigates some concerns.

    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% with one enum parameter. The description adds value by explaining why XML is default ('cross-referential reasoning') and what markdown offers ('leaner prose digest'), going beyond the schema's basic description.

    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 'Return everything the project records' and lists all components (header, connections, tables, joins), making the purpose unambiguous. It distinguishes from sibling tool 'aimm_init_project' by focusing on context retrieval.

    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 a clear directive: 'Always call this once at the start of a session before answering data-model questions', which tells the agent exactly when to use it. It also contrasts with other tools by stating this is the 'canonical context'.

    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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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.

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