mobile-docs-mcp

by zekkontro

Overview Schema Related Servers Score Discussions

TypeScript

Local

Server Quality Checklist

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

Latest release: v1.1.2
Disambiguation4/5
Each tool targets a distinct area: SDK listing, doc search, doc cache refresh, package search, package details, and GitHub issues. There is slight potential overlap between list_sdks and search_docs, but descriptions make the boundary clear.
Naming Consistency5/5
All tool names follow a consistent snake_case verb_noun pattern: list_, search_, refresh_, get_. No mixed conventions or vague verbs.
Tool Count5/5
Six tools is well-scoped for a mobile documentation and package-lookup server. Each tool serves a clear purpose without redundancy or bloat.
Completeness4/5
The core workflows—discovering SDKs, searching docs, looking up packages, and checking repository issues—are covered. Minor gaps exist such as no full-document retrieval endpoint, but agents can work around this via search_docs.
Average 3.5/5 across 6 of 6 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.
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 bears the full burden of behavioral disclosure. It states only that cached documentation is refreshed, but does not disclose side effects, idempotence, network dependence, scope of impact, or whether the operation is synchronous.
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 with the primary action front-loaded and the optional parameter stated immediately after. Every word contributes, with no filler or redundancy.
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 single-optional-parameter tool with no output schema, the description is minimally sufficient for basic invocation. However, it omits behavioral context such as what a refresh entails, whether it affects all cached docs at once, and when it should be called, leaving gaps for an agent.
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 schema already documents the sdk parameter at 100% coverage, so the description adds little new information. It does restate the optionality and scoping ('only that SDK'), which is helpful but does not extend the schema 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 states a specific verb ('refresh') and resource ('cached documentation'), making the operation unambiguous. The optional SDK scoping further distinguishes it from sibling read-oriented tools like list_sdks, search_docs, and search_packages.
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. It does not mention a trigger condition such as stale cache, nor does it suggest using sibling tools for searching or listing, leaving an agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden. It discloses that the operation is a search and that it returns sections rather than entire documents, which is useful. However, it does not explain matching behavior, result limits, default scope, or whether the sdk filter narrows the search across all docs.
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 short sentences with no filler. The action is front-loaded and the return behavior is stated in the second sentence. Every word earns its place.
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 tool is simple and the schema covers both parameters, but there is no output schema and the description only vaguely promises 'relevant sections.' It also omits any clarification about how the optional sdk filter interacts with the all-docs scope, leaving some context incomplete.
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 already documents both query and sdk parameters. The description does not add meaningful parameter-level detail beyond saying the search spans all SDK 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?
States a clear action (search), a specific resource (SDK documentation), and the expected result (relevant sections matching the query). It does not explicitly distinguish itself from sibling tool search_packages, but the resource target is specific enough to be understood.
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 provided about when to use this tool versus alternatives like search_packages or list_sdks. The description implies a broad documentation search but does not state when to prefer this tool or how the optional sdk filter should influence the choice.
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. It implies a read-only search action and lists registries, but it does not describe the result shape, result limits, pagination, network dependency, or any other behavioral characteristic the agent might need to anticipate.
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, front-loaded sentence with no filler. It efficiently communicates the action, resource, and scope in a compact form.
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 tool is simple with two required parameters and full schema coverage, so invoking it correctly is relatively easy. However, with no output schema and no annotations, the absence of any mention of return value or result shape leaves the description incomplete for an agent predicting what the call will produce.
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?
Both parameters are fully documented in the schema (query with examples, registry with enum values), so the baseline is 3. The description adds friendly registry names that map to the enum values, but otherwise provides no new parameter semantics 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?
The description uses a specific verb ('Search'), defines the resource ('packages/libraries'), and scopes it to a package registry with enumerable examples (npm, pub.dev, CocoaPods, Maven Central, PyPI). This clearly distinguishes it from siblings like search_docs, which targets documentation, and get_package_info, which would retrieve details for a known package.
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 explicit guidance is given on when to use this tool versus alternatives. It does not mention get_package_info for follow-up details on a specific package, nor does it contrast with search_docs for documentation searches. The only differentiation is the implicit 'package registry' phrase, which is not sufficient for confident tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 does disclose the concrete output categories, which is useful, but it does not mention behavior when the package is unknown, registry mismatches, or any auth/private-package considerations. The read-only nature is evident from 'get', but failure behavior is left unexplained.
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?
A single, front-loaded sentence that states the operation and immediately lists the useful return fields. There is no filler or redundant restatement of the tool name.
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 simple two-parameter lookup with no output schema, the description is almost complete: it names the tool's scope and return content while the schema handles parameter details. It could be more complete by explicitly steering agents to search_packages for discovery, but nothing essential for a correct call is missing.
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 already explains both parameters well, including examples for name and an enum for registry. The description adds no additional parameter-level meaning, which is acceptable given the high schema coverage.
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 states a clear verb-resource pair: 'Get detailed info about a specific package' and lists the exact return areas (latest version, license, homepage, repository). This distinguishes it from siblings like search_packages, though it does not explicitly name or contrast any sibling.
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 this tool is for retrieving details about an already-identified package, but it gives no explicit guidance about when to use it instead of search_packages or other sibling tools. There are no exclusions or alternative-routing hints.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 of behavioral disclosure. It usefully states that only open issues are returned and that they are recent, which conveys read-only behavior and scoping. However, it omits GitHub-specific traits an agent could encounter: the issues endpoint also returns pull requests, rate limits or auth may apply, and the exact sort ordering behind 'recent' is unspecified.
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 zero filler: the core function is front-loaded in sentence one and the use case in sentence two. The key scoping constraint ('open') is placed early, and every word contributes meaning.
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 simple 3-parameter list tool with no nested objects or output schema, the description plus the fully documented schema covers function, scope, and use case adequately. The notable gaps are the lack of any hint about the return payload shape (which matters since no output schema exists) and the pull-request-inclusion quirk of GitHub's issues API, both minor for a tool this straightforward.
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 coverage is 100%: repo includes owner/repo format with an example, limit has minimum, maximum, and default, and labels provides example values. The description adds no parameter-level detail, but since the schema fully documents every parameter, the baseline of 3 applies because the schema does the heavy lifting.
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 opens with a specific verb-object pair ('Get recent open GitHub issues') scoped to a repository, making the action and resource unambiguous. It also adds the use case ('checking known bugs and feature requests'), which clearly separates it from all sibling tools that concern docs, SDKs, and packages rather than issue tracking.
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 second sentence ('Useful for checking known bugs and feature requests') implies when the tool should be used, giving some context for issue triage and repo-health checks. However, it never states explicit conditions or names alternatives, and there is no when-not-to-use guidance. The risk is low because no sibling overlaps with GitHub issues, but the usage direction remains implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the full behavioral burden. It communicates that this is a non-mutating enumeration of sources and that output is categorized, but it does not disclose return shape, whether categories are top-level or per-source, or any auth/refresh behavior.
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?
A single sentence with no filler; the verb, object, scope, and categorization detail are all front-loaded. Every word earns its place.
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 parameterless listing tool with no output schema, this description is nearly complete. It defines what is listed ('all available SDK documentation sources') and the organization ('with categories'), though an explicit statement of the returned data shape would make it fully self-contained.
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 zero parameters and an empty input schema, which makes the schema coverage essentially 100%. Per the zero-parameter baseline, the description need not explain parameter behavior, and it doesn't introduce any conflicting parameter implications.
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 ('List') and resource ('all available SDK documentation sources'), and adds that results are grouped by categories. This clearly separates it from sibling tools like search_docs, refresh_docs, and the package/issue getters without requiring schema inspection.
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 about when to choose this tool over search_docs, refresh_docs, or search_packages. The intended use for discovering/cataloging sources is only implied by the tool name and sibling list, not stated.
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

Copy to your README.md:

[![mobile-mcp-docs MCP server](https://glama.ai/mcp/servers/zekkontro/mobile-mcp-docs/badges/card.svg)](https://glama.ai/mcp/servers/zekkontro/mobile-mcp-docs)

Score Badge

Copy to your README.md:

[![mobile-mcp-docs MCP server](https://glama.ai/mcp/servers/zekkontro/mobile-mcp-docs/badges/score.svg)](https://glama.ai/mcp/servers/zekkontro/mobile-mcp-docs)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

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/zekkontro/mobile-mcp-docs'

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