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arpitpaliwal007

mcp-multisource

by arpitpaliwal007
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Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool addresses a completely distinct concern: release_readiness provides aggregated cross-source data, system_health reports on source connectivity/status, and access_policy describes permissions. There is no overlap or ambiguity between their purposes.

    Naming Consistency5/5

    All tool names follow the same snake_case noun_noun pattern (release_readiness, system_health, access_policy), which is predictable and readable. No mixed conventions or vague verbs appear.

    Tool Count5/5

    Three tools is a well-scoped count for a focused multisource status server. Each tool earns its place and the set feels neither bloated nor too thin.

    Completeness4/5

    The server covers the core lifecycle of reading cross-source release readiness, monitoring source health, and checking access. A minor gap is the lack of a tool to query a specific source in detail or perform any mutating action, but agents can work around this with release_readiness and system_health.

  • Average 3.5/5 across 3 of 3 tools scored. Lowest: 2.8/5.

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

    • No community issues in the last 6 months
    • 1 commit 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

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  • This repository includes a README.md file.

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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

  • Behavior2/5

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

    With no annotations, the description carries the full burden of behavioral disclosure. It adds useful per-source scope and lists the health dimensions, but it does not say whether the call is read-only, whether probe triggers active network checks or side effects, or how circuit state and rate budget should be interpreted.

    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 compact fragment with no filler; the colon list is easy to scan and front-loads the core idea. It is arguably too sparse for full guidance, but the brevity itself is well executed.

    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?

    For a simple one-optional-parameter tool with an output schema, the core purpose is stated, but the probe parameter and safety/behavioral context are missing. It is minimally viable but leaves gaps an agent must guess around.

    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?

    The description never mentions the probe parameter, and schema description coverage is 0%, so the description adds no semantics beyond the schema's type and default. The parameter name is mildly self-explanatory, but the definition fails to state what different probe values actually do.

    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 phrase 'Per-source health' identifies the resource as source-level health status and enumerates what it covers: reachability, circuit state, rate budget, and last error. It lacks an explicit verb like 'get' or 'check,' so it is clear but not maximally precise and does not explicitly distinguish itself from release_readiness or access_policy.

    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 given on when to call this tool versus its siblings release_readiness or access_policy. There are no exclusion criteria, prerequisites, or context cues, so the agent must infer when this tool is the right choice.

    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 the full behavioral burden; it discloses the notable degraded/partial-result behavior and the `degraded` list, which is valuable. It does not state read-only status or permissions, but the aggregation context and failure-mode disclosure provide a solid behavioral 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 sentences with no filler; the core aggregation purpose is front-loaded and the degraded behavior is clearly stated. Very efficient for the information conveyed.

    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?

    The output schema covers return shape, so the description can focus on invocation context, but the ambiguous 'matching Notion specs' phrasing and lack of any guidance on optional parameters leave real gaps. The degraded-source caveat is good, yet an agent still does not know how to supply or shape `spec_query` or `team_key`.

    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 explain the parameters, but it only loosely references Notion specs and Linear work without mapping to `spec_query` or `team_key`. `repo` is implied by 'for a repo', but optional params and their string formats remain unexplained.

    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 identifies a specific aggregation task—open GitHub PRs/issues, Notion specs, and Linear tracked work for a repo—so an agent can infer the tool's role. It lacks a direct verb and does not contrast with the unrelated sibling tools, but the resource and actions are sufficiently concrete.

    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 phrase 'Cross-source snapshot' implies this is the go-to tool for combined release-readiness data, and the degraded-note signals when partial results should be expected. No explicit when-to-use/when-not-to-use guidance or alternative routing is provided, though the siblings appear unrelated.

    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 the full disclosure burden. It clearly signals a read-only, non-mutating operation via 'Show' and scopes the result to the current session. It does not discuss error conditions or side effects, but for a zero-parameter introspection tool this is adequate.

    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 focused sentence with no filler. It front-loads the action ('Show') and the important scoping information ('this session's role, capabilities and resource scopes') immediately.

    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?

    With zero parameters, an output schema, and a simple introspection purpose, the description provides all essential selection and invocation information. Nothing material is missing for an agent to understand when and how to call this 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?

    The tool has no parameters, so there is no parameter semantics burden. The schema is trivially complete, and the description cannot add parameter-level meaning. The baseline score of 4 applies.

    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 uses a specific verb 'Show' and identifies exactly what is exposed: this session's role, capabilities, and resource scopes. This clearly distinguishes the tool from sibling tools like release_readiness and system_health, which focus on different concerns.

    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 makes the usage context easy to infer: an agent needing session access details would choose this tool. However, it does not explicitly state when to use this tool versus alternatives, nor does it mention any exclusions or prerequisites.

    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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