Skip to main content
Glama
oratilo

oratilo-mcp

by oratilo
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v0.2.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: lookup is for checking a specific video ID/URL for existing coverage, while search is for discovering what content exists by topic when no specific URL is known. The descriptions explicitly clarify when to use each, leaving no ambiguity whatsoever.

    Naming Consistency4/5

    Both tools follow a consistent oratilo_ prefix with a clear verb (lookup, search). The pattern is consistent and predictable, though with only two tools the naming convention is minimally demonstrated.

    Tool Count3/5

    At 2 tools, the surface feels thin for a library with search and retrieval capabilities, though it could be argued these two operations are the core of what's needed. The count is borderline but reasonable given the limited scope described.

    Completeness3/5

    Core read operations (lookup and search) are covered, providing a functional surface. However, there are no write/contribution tools (e.g., adding or updating a summary), so the surface covers only the lookup half of the workflow and leaves the 'summarize it yourself' path entirely to the agent.

  • 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
    • 4 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

  • Behavior3/5

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

    With no annotations provided, the description carries the full burden. It discloses that results are ranked matches with page links and snippets, and that it searches across a library index (implying it's read-only). It doesn't disclose pagination behavior, rate limits, or what 'full text search' means regarding partial matches, but the read-only nature is reasonably implied by the search-then-lookup pattern.

    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, zero wasted words. The first sentence states purpose and when to use, the second describes the return format and follow-up action. Extremely efficient and front-loaded.

    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?

    For a search tool with no output schema and no annotations, the description is fairly complete: it explains the purpose, return format (ranked matches with page links and snippets), and directs follow-up. It could add what fields each result contains or note that 'full text of one' via lookup implies snippets come first, but it's adequate for a search-with-lookup sibling pattern.

    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?

    Schema description coverage is 100%, so the schema fully documents all three parameters (query, lang, limit). The description mentions searching by topic/phrase/person/company which maps to query usage, and notes the AND-ed multi-word behavior is in the schema. The description adds minor value by framing what query types are useful but doesn't go much 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?

    Description uses a specific verb (Search) with a specific resource (full text of oratilo summaries) and enumerates search dimensions (topic, phrase, person, company). It clearly distinguishes from the sibling tool by explaining when this one is appropriate (when you do not have a specific video URL) and directs follow-up to oratilo_lookup for full text of one result.

    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 gives clear context on when to use this tool ('when you do not have a specific video URL but want to know what the library covers') and directs users to oratilo_lookup for full text retrieval. However, it doesn't explicitly state when to NOT use this tool or contrast with the sibling for the lookup use case.

    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 provided, so the description carries the full burden. It discloses the hit/miss behavior ('Returns the full summary plus provenance metadata on a hit. A miss means the video is not covered'), which is valuable behavioral context beyond what a schema would convey. It lacks some depth (e.g., what provenance metadata entails on a miss) but covers the core behavioral contract well.

    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?

    Three focused sentences, well front-loaded with the decision-critical use case. Each sentence earns its place: purpose, hit/miss outcome, and accepted input formats. Minor redundancy with the schema's source description but overall tight and 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?

    For a read-lookup tool with a simple 2-param schema and no output schema, the description is reasonably complete. It explains the decision workflow, hit/miss outcome, and accepted input types. It could benefit from noting what's returned on a miss and clarifying that this isn't a fuzzy search (contrast with sibling), but given the tool's simplicity the coverage is strong.

    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 description coverage is 100% and both parameters are described adequately in the schema. The description adds value by explaining accepted source formats ('YouTube URLs or video ids') and noting arXiv ids are 'reserved for the future,' which constrains interpretation beyond the raw schema text. Baseline 3 raised for the added source-format context.

    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 ('Check whether') plus resource ('oratilo... has a summary') plus context ('BEFORE spending work summarizing it yourself'). It clearly distinguishes from the sibling tool oratilo_search by indicating this is an exact lookup vs search. The purpose is unambiguous and actionable.

    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?

    Explicit when-to-use guidance: 'Check... BEFORE spending work summarizing it yourself.' It also states what to do on a miss ('summarize it yourself'), providing exclusions. Distinguishes from alternatives by naming when not to rely on it.

    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

oratilo-mcp MCP server

Copy to your README.md:

Score Badge

oratilo-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/oratilo/oratilo-mcp'

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