mcp-tap

by felipestenzel

Overview Schema Related Servers Score Discussions

Python

Local

Server Quality Checklist

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

Latest release: v0.6.9
Disambiguation5/5
Every tool has a clearly distinct purpose covering different aspects of MCP server management: search, scan, install, test, list, remove, restore, verify, and health check. No two tools overlap in function.
Naming Consistency5/5
All tool names follow a consistent verb_noun snake_case pattern (e.g., configure_server, test_connection). There are no deviations or mixed conventions, making the set predictable and easy to navigate.
Tool Count5/5
With 11 tools, the server covers the full lifecycle of MCP server management—discovery, installation, configuration, testing, removal, and verification—without being too sparse or overly extensive. Each tool earns its place.
Completeness4/5
The tool set covers the core lifecycle comprehensively: search, scan, install, test, health check, list, remove, restore, and verify. Minor gaps exist, such as no dedicated update tool, but the workflow is largely complete and users can work around missing operations.
Average 4.6/5 across 11 of 11 tools scored. Lowest: 4/5.
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
- Last stable release on February 25, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description contradicts the annotation readOnlyHint: true by stating the auto_heal parameter attempts to 'fix each unhealthy server automatically,' implying write operations. This is a serious inconsistency, warranting a score of 1 and flagging annotation contradiction.
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?
Well-structured with a clear summary, details, and explicit Args/Returns sections. Brief but informative. Slight room for improvement in trimming redundant phrases (e.g., 'at once' could be implied).
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?
Covers purpose, mechanism, usage guidance, parameters, and return value summary. Given the output schema exists (so return details are not strictly needed) and the complexity of health checking, the description is reasonably complete. Lacks mention of concurrency limits or error handling specifics.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage, yet the description provides thorough explanations for all three parameters: client options with auto-detection, timeout_seconds clamping and default, auto_heal behavior and its effect on the report. This adds significant value beyond the bare 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 clearly states it checks health of all configured MCP servers at once, with specific details on how it does so (concurrently, via MCP protocol, calling list_tools). This distinguishes it from sibling tools like configure_server, test_connection, and verify.
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?
Explicitly states to use after configure_server for verification or as periodic health check. Provides alternative course of action for unhealthy servers (remove_server + configure_server). Clear when-to-use and guidance.
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?
Description explains the read-only behavior (reads lockfile and compares), consistent with annotations (readOnlyHint: true). Provides context on what happens (report drift) without contradicting annotations.
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?
Description is well-structured with summary, Args, and Returns sections. It is front-loaded with purpose and contains no unnecessary sentences, though slightly verbose.
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?
Given the tool's complexity (2 params, read-only, output schema exists), the description is fully complete: explains purpose, parameters, and return value without gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explaining both parameters: project_path as root directory with lockfile, and client as MCP client with enumerated values and auto-detection.
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 function: compare lockfile with installed MCP servers and report drift. It differentiates from siblings like list_installed or check_health by specifying the comparison aspect.
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 after configuration or installation but does not explicitly state when to use this tool versus alternatives like restore or apply_stack. No exclusions or alternative tools are mentioned.
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?
Annotations provide destructiveHint: true. The description confirms installation action and highlights dry_run=True for preview without changes. Adds details about return values (per-server results, env_vars_needed). No contradiction with annotations.
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?
Well-structured with a clear introductory sentence, explanation of stacks, and a clean list of arguments. Each sentence adds value. Not overly verbose, though slightly longer than necessary for a tool with many parameters.
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?
Given the tool's complexity (5 parameters, destructive action, output schema), the description covers all aspects: purpose, usage, parameters, dry run capability, and return structure. No missing information for an agent to use the tool correctly.
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 0%, so description compensates. Clearly explains each parameter: stack (built-in name or .yaml path), clients and scope ('Same as configure_server'), project_path (required when scope='project'), and dry_run (preview without changes). Adds meaning 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?
Clearly states 'Install a group of MCP servers from a stack definition.' The verb 'install' and resource 'group of MCP servers' are specific. The concept of stacks (shareable profiles) distinguishes it from sibling tools like configure_server or list_installed.
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?
Explains that stacks are shareable profiles and mentions built-in stacks (data-science, web-dev, devops) and the option to use a .yaml file. Provides context for when to use each. However, does not explicitly state when not to use this tool or suggest alternatives.
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?
The annotation destructiveHint=true already flags destruction. The description adds the behavioral detail that the user must restart the client for changes to take effect, which is valuable beyond the annotation.
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?
Well-structured with clear sections and bullet-like Args. Every sentence adds value, but could be slightly more concise. Still earns its place.
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 an output schema present, the description still explains return values (success status, per_client_results). It covers all necessary context for a removal tool, including restart requirement.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully explains each parameter: server_name (exact name), clients (comma-separated, 'all', or auto-detect), scope ('user' default vs 'project'), project_path (required for project scope). Also describes return values.
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 it removes an MCP server from AI client configuration, using specific verb 'remove' and resource 'MCP server from AI client's configuration'. It distinguishes from siblings like configure_server, inspect_server, and list_installed.
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?
Provides explicit usage context: suggests using list_installed to see server names, explains clients parameter with 'all' option. While it doesn't explicitly say when not to use, the context is clear and helpful.
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?
Annotations include destructiveHint: true, and the description confirms the tool modifies configuration by reinstalling servers. It also discloses limitations about env vars and the dry_run option, adding value beyond annotations.
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 well-structured with a summary, docstring-style parameter details, and a returns section. It is informative but could be slightly more concise without losing clarity.
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?
Given the complexity of restoring from a lockfile with multiple clients, the description covers input parameters, behavior, and return values (per-server status, env vars, success). Output schema exists, so return description is adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description provides detailed explanations for all three parameters: project_path (root directory), client (comma-separated names, 'all', or auto-detect), and dry_run (shows without installing). This compensates fully.
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 specific verb 'Recreate' and resource 'MCP server configurations from a lockfile', clearly distinguishing it from siblings like configure_server or remove_server.
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 states the tool reads a lockfile and reinstall servers, and explicitly notes that environment variable values are not stored, requiring manual setting. It does not explicitly state when not to use it or provide alternatives, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description details the full behavioral lifecycle: spawns the server process, connects via MCP stdio, calls list_tools(), then shuts down cleanly. It also covers the auto_heal feature's behavior (attempt diagnosis, apply fix, retry). This goes well beyond the readOnlyHint annotation, providing transparency about what the tool does internally.
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 well-structured: a summary sentence, step-by-step behavioral explanation, usage context, and parameter explanations. It is slightly lengthy but every sentence adds value. It could be trimmed slightly, but overall it's efficiently organized.
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 presence of an output schema (implied by context), the description covers the tool's purpose, parameters, behavior, and return value (success status, tool names, error message, optional healing details). It is comprehensive enough for an agent to decide when and how to use it, though it could mention that the test only checks the list_tools response, not full functionality.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 0% schema description coverage, the description explains all four parameters in a dedicated 'Args' block. It adds context like server_name must match config exactly, client values are listed, timeout is clamped to 5-60, and auto_heal returns healing details. This fully compensates for the schema's lack of descriptions.
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 tests a single configured MCP server, using specific verbs ('test', 'verify', 'debug') and identifies the resource ('MCP server'). It distinguishes from siblings by mentioning specific use cases like after configure_server or when check_health is unhealthy, making the purpose unique and unambiguous.
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 explicitly tells when to use the tool: 'after configure_server, or to debug a server that check_health reported as unhealthy.' It also provides parameter-level hints (e.g., auto_heal for automatic fixes). However, it does not explicitly exclude alternatives like inspect_server or list_installed, so it misses some direct comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description details the three-step process, validation on failure, dry-run behavior, and HTTP transport behavior, far exceeding the single destructiveHint annotation.
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 long but well-organized with numbered steps and parameter bullets. Some redundancy exists, but overall it is efficient and front-loaded.
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?
The description covers the complete workflow, edge cases (dry run, HTTP transport, validation failure), parameter usage, and return values, making it self-sufficient for a complex tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema coverage, the description fully explains all 10 parameters, including sources and defaults, providing everything an agent needs to invoke the tool correctly.
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 installs an MCP server package, adds it to client config, and verifies it. It calls itself 'the main action tool', which distinguishes it from sibling tools like remove_server or list_installed.
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 explains when to use this tool (for complete setup) and provides context for HTTP servers, but does not explicitly contrast with alternatives or state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description aligns with annotations (readOnlyHint=true) and explains the scan's behavior: analyzing files, cross-referencing with installed servers, and providing recommendations. No contradictions.
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 well-structured: a brief summary, then bulleted details on what it does, followed by explicit Args and Returns sections. Every sentence adds value, and the length is appropriate.
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?
Given the tool's complexity (scans, cross-references, recommendations), the description covers all aspects: input parameters, return values (detected_technologies, recommendations with already_installed flag), and next steps. The presence of an output schema further reduces burden.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description explains both parameters beyond the schema: 'path' defaults to current directory, 'client' auto-detects. With 0% schema coverage, the description fully compensates, adding meaning like 'auto-detects' which is not in 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 clearly states the tool's purpose: scanning a project directory to detect tech stack and recommend MCP servers. It differentiates itself from sibling tools by positioning as 'the best starting point' and specifies the types of files analyzed.
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 suggests this is the starting point and mentions using 'configure_server' to install recommendations. It provides clear context for when to use the tool, though it could be more explicit about when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that extracted data may be incomplete or ambiguous, instructing the agent to use judgment. It also mentions that the raw README is returned for LLM reasoning. This adds value beyond the 'readOnlyHint' annotation. No contradiction with annotations.
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 structured into clear paragraphs with headings ('Args:', 'Returns:') and bullets. It is front-loaded with the core purpose. While slightly verbose, every sentence adds value. Minor improvement would be trimming redundant phrases.
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?
The description explains the return structure (extracted_config, raw_readme, confidence) and what is extracted (install commands, transport type, env vars, etc.). Given the tool's single parameter and the existence of an output schema, the description provides sufficient context for the agent to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one parameter ('repository_url') with no description. The description compensates fully by providing a detailed explanation in the 'Args' section, including an example ('e.g. "https://github.com/modelcontextprotocol/servers"'). This adds significant meaning 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 clearly states the tool fetches documentation and extracts configuration details from MCP server repositories. It uses specific verbs ('fetch', 'extract') and identifies the resource ('MCP server's documentation'). It explicitly distinguishes from the sibling tool 'search_servers' by specifying when to use this tool instead.
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 explicitly states when to use the tool: 'when search_servers returns incomplete data' or 'when you have a GitHub URL for a server not in the registry.' This provides clear context and an alternative, guiding the agent on appropriate invocation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=true, and description adds important behavioral detail about automatic masking of secret-looking environment variables. No contradictions.
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?
Description is well-organized with a clear first line, usage guidance, behavioral note, and parameter breakdown. Slightly wordy but efficient given the detail provided.
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?
Given the presence of output schema (context signal), the description adequately covers purpose, usage, behavioral details, parameters, and return structure. No gaps identified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage, but description fully explains both parameters: client lists valid values (Claude Desktop, Claude Code, Cursor, Windsurf) and auto-detection behavior; project_path describes enrichment with additional fields. This adds critical meaning 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 clearly states it lists all configured MCP servers. The verb 'List' and resource 'servers' are specific, and the tool is distinct from siblings like configure_server, test_connection, and remove_server.
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?
Explicitly advises using this tool before adding new servers or to find server names for other tools. Also mentions auto-detection of client and optional enrichment with project_path.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=true, and the description confirms it's a search operation. It adds extensive behavioral details: ranking by project_path, maturity evaluation, credential_status, and deterministic ranking metadata.
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 well-structured with clear sections (Args, Returns) and is front-loaded with the main purpose. While somewhat lengthy, every sentence adds valuable information.
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?
Given 4 parameters and an output schema, the description covers all input semantics and return fields comprehensively. It even explains conditional fields (credential_status, maturity).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description carries full burden. It explains each parameter's purpose and effect: query (search term), limit (max results), project_path (relevance ranking), evaluate (GitHub signals).
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 it searches the MCP Registry for servers matching a keyword, with a specific verb and resource. It distinguishes itself from siblings like configure_server (install) and list_installed (view installed).
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?
Explicitly says 'Use this when the user asks for a specific server or technology.' It also advises when to set evaluate=False for faster searches and directs to use configure_server after finding the server.
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:

[![mcp-tap MCP server](https://glama.ai/mcp/servers/felipestenzel/mcp-tap/badges/card.svg)](https://glama.ai/mcp/servers/felipestenzel/mcp-tap)

Score Badge

Copy to your README.md:

[![mcp-tap MCP server](https://glama.ai/mcp/servers/felipestenzel/mcp-tap/badges/score.svg)](https://glama.ai/mcp/servers/felipestenzel/mcp-tap)

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/felipestenzel/mcp-tap'

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