Skip to main content
Glama
KitsuneTech1

Token Guardian MCP

by KitsuneTech1
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Local

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 serves a distinct, non-overlapping purpose: one provides a usage snapshot with improvement suggestions, the other recommends a routing choice for a task. There is no ambiguity between them.

    Naming Consistency5/5

    Both tool names follow a consistent 'token_guardian_<verb>_<noun>' pattern using snake_case, with descriptive verbs ('usage_snapshot', 'recommend_route') that clearly indicate their function.

    Tool Count3/5

    With only 2 tools, the server feels minimal for a domain that might benefit from additional diagnostics (e.g., cost breakdown, model listing) or configuration advice. The count is on the low end of acceptable for a focused utility.

    Completeness2/5

    The server explicitly avoids any action tools, being read-only. It covers snapshot analysis and routing recommendations but lacks tools for detailed queries (e.g., by time period, by model) or applying any changes, leaving notable gaps for hands-on usage optimization.

  • Average 3.8/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 3 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

  • 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.

  • 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

  • Behavior4/5

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

    Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context: 'Returns advice only and never switches the active client', confirming the tool's side-effect-free nature. No contradiction with annotations. The description could mention error handling or latency but is sufficient given the strong annotation coverage.

    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 key action, and contains no filler. Every word adds value: 'conservative' qualifies the recommendation, 'one task' sets scope, and 'never switches' clarifies behavior. Ideal conciseness.

    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 the tool's complexity (7 parameters, output schema exists, good annotations), the description covers the core workflow but leaves out context like the meaning of 'conservative', 'effort', or how the recommendation relates to the input parameters. The output schema might fill gaps, but the description could be more helpful for understanding the tool's role in a multi-step workflow.

    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 only 43% (3 of 7 parameters have descriptions). The description adds no parameter-specific information; it only states the overall purpose. Parameters like 'risk', 'current_effort', and 'context_tokens' remain undocumented, and the description does not compensate for the low coverage. The agent must rely on the incomplete schema, which is insufficient.

    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 verb 'recommend', the resource ('model and effort'), and the scope ('for one task'). It also distinguishes from the sibling tool 'token_guardian_usage_snapshot' by emphasizing that it returns advice only and does not change state, making the purpose specific and unambiguous.

    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?

    The description provides no explicit guidance on when to use this tool versus the sibling 'token_guardian_usage_snapshot'. It states that it returns advice only, implying safe usage, but does not mention when to choose recommendations over usage snapshots or any prerequisites or exclusions. This leaves the agent without clear direction on invocation 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?

    Annotations already declare readOnlyHint, idempotentHint, and destructiveHint as safe. The description reinforces this by stating 'Never changes settings or sessions.' It adds value by describing what the tool does with the data (identify, return quick wins), which goes beyond the annotations' safety profile.

    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 efficient sentences that pack in the tool's purpose, outcomes, and safety guarantee. Every word serves a purpose with no redundancy or filler.

    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?

    The description is adequate for a read-only snapshot tool with an output schema and sensible defaults, but it fails to mention the tunable parameters (days, agent, top_sessions) that control the snapshot scope. The sibling tool is not contrasted, leaving the agent to infer the relationship.

    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%, so the description must compensate, but it does not mention the three parameters (days, agent, top_sessions) or their roles. The parameter names are somewhat self-explanatory, but the description adds no additional meaning beyond the schema's type and default constraints.

    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 reads usage data, identifies large contexts and expensive routing, and returns quick wins. It uses a specific verb ('Read') and resource ('local Claude Code and Codex usage'), and distinguishes from the sibling tool 'token_guardian_recommend_route' by focusing on analysis rather than recommendations.

    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 it never changes settings or sessions, implying it is safe for inspection. It contrasts with the sibling by focusing on analysis and quick wins, but does not provide explicit 'when to use vs when not to use' guidance. The context is clear enough for an agent to infer appropriate usage.

    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

token-guardian-mcp MCP server

Copy to your README.md:

Score Badge

token-guardian-mcp 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/KitsuneTech1/token-guardian-mcp'

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