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noga7

MCP Content Credentials Server

by noga7
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Hybrid

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes based on input source: read_credentials_file handles local files while read_credentials_url handles URLs. Their descriptions explicitly specify when to use each tool, eliminating any potential confusion about which to select for a given scenario.

    Naming Consistency5/5

    Both tools follow an identical verb_noun pattern (read_credentials_file and read_credentials_url) with perfect consistency. The naming clearly indicates the action (read) and distinguishes the input type (file vs URL) in a predictable manner.

    Tool Count3/5

    With only 2 tools, the server feels somewhat thin for a Content Credentials domain that could benefit from additional operations like validation, manifest listing, or credential creation. While the two tools cover the core reading functionality from different sources, the scope seems limited compared to what a comprehensive C2PA/credentials server might offer.

    Completeness2/5

    The server only provides read operations from two input sources, missing essential capabilities for a Content Credentials domain. There are no tools for creating, updating, validating, or managing credentials, nor tools to list available manifests or check credential status. This creates significant gaps that would limit agent workflows beyond basic information retrieval.

  • Average 4.3/5 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 status not available

  • This repository is licensed under MIT License.

  • 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

  • Behavior4/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior, including how to process user questions (specific vs. general), the order of information presentation, and rules for skipping sections or omitting data. However, it lacks details on error handling, file format limitations, or performance aspects like rate limits.

    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 front-loaded with the purpose and usage guidelines, but it becomes verbose with detailed response instructions that might be better suited for an output schema or separate documentation. While informative, some sentences could be streamlined to improve conciseness without losing essential guidance.

    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 (involving file reading and credential parsing) and lack of annotations or output schema, the description does a good job of covering usage scenarios and behavioral expectations. However, it could be more complete by addressing potential errors, file size limits, or authentication needs, which are relevant for such operations.

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

    Parameters3/5

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

    The input schema has 100% description coverage, providing a clear parameter 'filePath' with examples. The description does not add significant meaning beyond the schema, as it doesn't elaborate on parameter usage or constraints. Given the high schema coverage, a baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

    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 purpose as 'Read Content Credentials from a local file,' specifying both the verb ('Read') and resource ('Content Credentials from a local file'). It distinguishes from the sibling tool 'read_credentials_url' by explicitly focusing on local files rather than URLs, making the distinction clear.

    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 provides explicit guidance on when to use this tool: 'when the user drops a file or provides a file path AND asks questions like...' and lists specific question types. It also distinguishes usage from alternatives by implying this is for local files, contrasting with the sibling tool for URLs, and offers detailed instructions on how to respond based on user queries.

    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 provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it downloads files temporarily, checks for C2PA manifests, cleans up afterward, and outlines the structured response format. However, it doesn't mention potential errors (e.g., invalid URLs, network issues) or performance characteristics like rate limits, leaving some gaps.

    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 appropriately sized and front-loaded with the core purpose and usage guidelines. However, it includes extensive formatting instructions (e.g., bullet points, section ordering) that might be better suited for an output schema or separate documentation, slightly reducing efficiency. Most sentences are necessary for clarity.

    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 (involving file download, C2PA parsing, and structured responses) and lack of annotations/output schema, the description does a good job covering behavior and usage. It explains the processing steps and response format in detail. However, without an output schema, the description must fully define return values, which it does partially but could be more explicit about data types or error cases.

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

    Parameters3/5

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

    The input schema has 100% description coverage, so the baseline is 3. The description doesn't add any parameter-specific information beyond what's in the schema (which already defines 'url' as an HTTP/HTTPS URL). No additional syntax, format, or constraints are provided in the 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 clearly states the specific action ('Read Content Credentials from a file at a URL'), identifies the resource (file at URL), and distinguishes it from its sibling 'read_credentials_file' by specifying URL-based input rather than file-based input. The verb 'read' is precise and the scope is well-defined.

    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 provides explicit guidance on when to use this tool ('when the user provides a URL AND asks questions like...'), includes specific example questions (e.g., 'who made this', 'is this AI'), and mentions the sibling tool context. It also gives detailed instructions on how to respond based on user intent (specific vs. general questions), making it highly actionable.

    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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