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ShiftinBits

Constellation

by ShiftinBits
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Hybrid

Server Quality Checklist

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

  • Latest release: v1.2.8

  • Disambiguation5/5

    With only one tool, there is no ambiguity; the tool's purpose is clearly defined and distinct from any other.

    Naming Consistency5/5

    A single tool name ensures perfect naming consistency; there are no naming conflicts or inconsistencies.

    Tool Count2/5

    The server attempts to cover a broad range of code intelligence operations (symbol search, dependency analysis, impact analysis, etc.) but condenses them into a single tool, which is insufficient for discoverability and typical MCP expectations.

    Completeness4/5

    The tool's API covers many code intelligence operations (symbols, dependencies, impact, dead code), with minor gaps like source reading explicitly delegated elsewhere, making it fairly complete for its stated purpose.

  • Average 4.7/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 17 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 AGPL 3.0.

  • 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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  "$schema": "https://glama.ai/mcp/schemas/server.json",
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  ]
}

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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.

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

  • Behavior5/5

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

    Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral context: sandbox limits (50 api calls, 128 MB memory, 100 KB code), timeout derivation, UNSUPPORTED_LANGUAGE rejection, and requirement for cwd to locate constellation.json. These reveal important constraints and behaviors beyond annotations.

    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 thorough but verbose. It uses headings, bullet points, and code examples effectively, and front-loads with the decision rule. However, some sections (e.g., sandbox limits, top 5 questions) are longer than necessary. A slightly more concise version would maintain completeness while improving scanability.

    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 the tool's complexity (multiple API methods, sandbox restrictions, dynamic timeouts, language support), the description covers all essential aspects: when to use, how to compose calls, error conditions (UNSUPPORTED_LANGUAGE), and limitations. Output schema exists, so return value documentation is not needed. Complete for its context.

    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%, but description adds meaning: for 'code', it lists available API methods; for 'timeout', explains dynamic derivation and clamping; for 'cwd', explains its role in locating configuration. This enriches the agent's understanding beyond the schema, though the schema already covers parameter types and descriptions.

    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 is for structure questions (definitions, callers, impact) versus text search (Grep). It uses specific verbs like 'find', 'trace', 'search' and explicitly distinguishes from sibling tools (Grep, Glob, Read) even though no siblings are listed. The 'WRONG TOOL SIGNAL' and 'NOT FOR' sections reinforce purpose clarity.

    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?

    Excellent guidelines: 'DECISION RULE' distinguishes structure vs text; 'USE IMMEDIATELY WHEN' lists specific scenarios before editing, exploring, refactoring; 'TOP 5 QUESTIONS' maps common intents to methods; 'NOT FOR' lists exclusions. Also provides workflow examples and wrong-tool signals.

    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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