Skip to main content
Glama
AnshuML

Istedlal MCP Server

by AnshuML

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: get_file_metadata retrieves specific file details by ID, search_files filters files based on explicit metadata criteria, and semantic_search_files finds files using natural language queries over embeddings. There is no overlap in functionality, making tool selection straightforward for an agent.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with snake_case: get_file_metadata, search_files, and semantic_search_files. The naming is predictable and readable, with no deviations in style or convention across the set.

    Tool Count3/5

    With only 3 tools, the server feels thin for a file management domain, as it lacks essential operations like upload, delete, or update files. While the tools are well-defined, the count is borderline low for covering typical file lifecycle needs, potentially limiting agent workflows.

    Completeness2/5

    The tool set has significant gaps for file management: there are no tools for uploading, deleting, updating, or processing files, which are core operations in this domain. Agents will face dead ends when trying to perform basic file lifecycle tasks, as the surface only supports retrieval and search functions.

  • Average 2.9/5 across 3 of 3 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
  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • 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

  • Behavior2/5

    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 mentions the return details (filename, mime_type, etc.), which is helpful, but fails to cover critical aspects like whether this is a read-only operation, potential error conditions (e.g., invalid ID), authentication needs, or rate limits. This leaves significant gaps for a tool with 3 required parameters.

    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 concise and front-loaded, with two sentences that directly state the purpose and return details. There is no wasted text, making it efficient, though it could be slightly more structured by explicitly listing parameters or usage scenarios.

    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?

    Given the complexity (3 required parameters, no annotations, no output schema), the description is incomplete. It covers the basic purpose and return fields but misses parameter semantics, behavioral traits like error handling, and lacks output schema details. This is inadequate for a tool with multiple required inputs and no structured safety hints.

    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 schema description coverage is 0%, so the description must compensate for all 3 parameters. It only mentions 'file_id' indirectly ('by ID'), but provides no context for 'tenant_id' or 'project_id', such as their roles or how they relate to the file. This adds minimal value beyond the schema, failing to clarify parameter meanings adequately.

    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 clearly states the verb 'fetch' and resource 'metadata for a specific file by ID', making the purpose evident. It distinguishes from siblings like 'search_files' by focusing on a single file rather than searching. However, it doesn't explicitly contrast with 'semantic_search_files', so it's not fully differentiated.

    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 guidance on when to use this tool versus alternatives like 'search_files' or 'semantic_search_files'. It implies usage for retrieving metadata of a known file ID, but lacks explicit when/when-not instructions or prerequisites, leaving the agent to infer context.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

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

    With no annotations provided, the description carries full burden but only mentions search functionality and filter examples. It lacks critical behavioral details: whether this is read-only, pagination behavior (implied by parameters but not described), rate limits, authentication needs, or what happens when no filters are provided. The description doesn't contradict annotations, but provides minimal behavioral context.

    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 concise with two sentences that efficiently convey core functionality and filter examples. It's front-loaded with the main purpose. No wasted words, though it could be slightly more structured with explicit parameter guidance.

    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?

    Given 5 parameters with 0% schema coverage, no annotations, and no output schema, the description is incomplete. It covers the 'filters' parameter well but ignores others, lacks behavioral transparency for a search tool, and doesn't explain return values or error conditions. This leaves significant gaps for agent understanding.

    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 only partially does. It mentions 'metadata filters' and examples like 'filename, mime_type, processing_status, upload date range', which helps explain the 'filters' parameter. However, it doesn't address 'tenant_id', 'project_id', 'page', or 'page_size' parameters, leaving 4 of 5 parameters without semantic clarification.

    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 clearly states the verb ('search') and resource ('files') with the method ('using metadata filters'). It distinguishes from 'get_file_metadata' by focusing on search rather than retrieval, and from 'semantic_search_files' by specifying metadata-based rather than semantic search. However, it doesn't explicitly name these distinctions.

    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 implies usage context through 'metadata filters' and example filters, suggesting when to use this tool. However, it doesn't explicitly state when to choose this over 'semantic_search_files' or 'get_file_metadata', nor does it mention prerequisites like required parameters.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

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

    No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions 'semantic search' and 'natural language', it doesn't describe how results are returned (e.g., relevance scores, format), whether it's read-only (implied but not stated), performance characteristics, or any limitations like rate limits or authentication needs. The description is too vague for a tool with 6 parameters and no annotations.

    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 extremely concise with two sentences that are front-loaded and waste no words. Every phrase ('Semantic search over file embeddings', 'Use natural language to find relevant content across files') directly contributes to understanding the tool's purpose.

    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?

    Given the complexity (6 parameters, 3 required), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns, how to interpret results, or provide any details about parameter usage. For a search tool with multiple filtering options, this leaves significant gaps for an AI agent to use it correctly.

    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%, meaning none of the 6 parameters have descriptions in the schema. The tool description provides no information about parameters like 'tenant_id', 'project_id', 'top_k', 'file_ids', or 'threshold', leaving their purposes and usage completely undocumented. The description fails to compensate for the lack of schema documentation.

    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 clearly states the tool's purpose: 'Semantic search over file embeddings' with the specific action 'Use natural language to find relevant content across files.' It distinguishes itself from 'search_files' by specifying semantic search (natural language understanding) rather than keyword-based search, though it doesn't explicitly contrast with 'get_file_metadata'.

    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 implies usage context ('Use natural language to find relevant content') but doesn't explicitly state when to use this tool versus alternatives like 'search_files' or 'get_file_metadata'. It provides no guidance on prerequisites, exclusions, or specific scenarios where this tool is preferred.

    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

MCP MCP server

Copy to your README.md:

Score Badge

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/AnshuML/MCP'

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