Tribal Knowledge Service

Server Quality Checklist

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

Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes: track_error creates records, get_error_by_id retrieves single records, delete_error removes them, search_errors filters by multiple criteria, and find_similar_errors performs semantic similarity searches. However, search_errors and find_similar_errors could potentially be confused as both search for errors, though their approaches differ (filtering vs. similarity).
Naming Consistency5/5
All tools follow a consistent verb_noun pattern with snake_case: delete_error, find_similar_errors, get_api_status, get_error_by_id, search_errors, and track_error. The naming is predictable and readable throughout the set.
Tool Count5/5
With 6 tools, this is well-scoped for a knowledge base service focused on error tracking. Each tool serves a clear purpose (CRUD operations, searching, and status checks), and none feel redundant or missing given the domain.
Completeness4/5
The toolset covers core CRUD operations (create via track_error, read via get_error_by_id, delete via delete_error) and searching (search_errors, find_similar_errors), with get_api_status for monitoring. A minor gap is the lack of an update tool to modify existing error records, which agents might need to work around by deleting and recreating.
Average 3.1/5 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?
No annotations are provided, so the description carries full burden. It mentions returning a list of similar error records but doesn't disclose behavioral traits such as how similarity is determined, whether it's read-only, performance characteristics, or error handling. This is a significant gap for a search tool with zero annotation coverage.
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. The Args and Returns sections are structured clearly, though the 'Returns' part could be more specific. Every sentence adds value, but it could be slightly more concise by integrating the sections more fluidly.
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 of a similarity search tool with no annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't explain what 'similar' means, the return format beyond 'List of similar error records', or how results are ordered, leaving gaps for the agent to infer behavior.
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 0%, so the schema provides no parameter descriptions. The description adds basic semantics for 'query' ('Text to search for in the knowledge base') and 'max_results' ('Maximum number of results to return'), which compensates partially but lacks details like format constraints or default behavior beyond the schema's default value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Find errors similar to the given query' which provides a clear verb ('Find') and resource ('errors'), but it's vague about what constitutes 'similar' and doesn't distinguish from sibling tools like 'search_errors'. It's not tautological but lacks specificity about the similarity mechanism.
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 on when to use this tool versus alternatives like 'search_errors' or 'get_error_by_id'. The description implies usage for finding similar errors but doesn't specify contexts, prerequisites, or exclusions, leaving the agent to guess based on tool names alone.
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. It states the tool 'Check[s] the API status' and 'Returns API status information', but doesn't disclose behavioral traits like whether it's read-only, requires authentication, has rate limits, or what specific information is included in the return. For a tool with zero annotation coverage, this is a significant gap.
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 concise with two sentences, but it's not front-loaded effectively. The first sentence states the purpose, but the second ('Returns: API status information') is redundant and doesn't add value beyond what's implied. It could be more structured to emphasize key details.
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 lack of annotations and output schema, the description is incomplete. It doesn't explain what 'API status information' includes, how it's formatted, or any behavioral context. For a tool that might be critical for monitoring, this leaves too many gaps for effective agent use.
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 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description doesn't add parameter semantics, but this is appropriate given the lack of parameters, warranting a baseline score of 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool 'Check[s] the API status', which is a clear verb+resource combination. However, it doesn't differentiate this from sibling tools like 'track_error' or 'get_error_by_id' that might also provide status-related information, nor does it specify what aspects of API status are checked (health, uptime, version, etc.).
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 on when to use this tool versus alternatives. The description doesn't mention prerequisites, timing, or how it differs from sibling tools like 'track_error' or 'search_errors' that might overlap in monitoring contexts. This leaves the agent with no usage 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool searches and returns a list, but lacks critical details: whether it's read-only, how results are ordered, if there's pagination, error handling, or performance characteristics. For a search tool with 7 parameters, this leaves significant gaps in understanding its behavior.
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 uses bullet points for parameters, making it easy to scan. It's appropriately sized for a tool with 7 parameters, though the parameter explanations are minimal. There's no wasted text, but it could be more front-loaded with key usage information.
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 (7 parameters, no annotations, no output schema), the description is incomplete. It covers the basic purpose and parameters but lacks behavioral details, usage guidelines, and output specifics. For a search tool in a knowledge base context, this leaves the agent with insufficient information to use it effectively without trial and error.
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 description lists all 7 parameters with brief labels, but schema description coverage is 0%, so the schema provides no additional documentation. The description adds basic semantic context (e.g., 'error_type: Type of error to filter by'), which helps interpret parameters beyond their names. However, it doesn't explain formats, constraints, or interactions between parameters, leaving room for ambiguity.
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: 'Search for errors in the knowledge base.' This specifies the verb ('search') and resource ('errors in the knowledge base'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'find_similar_errors' or 'get_error_by_id', which prevents a perfect score.
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 doesn't mention sibling tools like 'find_similar_errors' or 'get_error_by_id', nor does it specify contexts where this search tool is preferred. The agent must infer usage from the tool name alone, which is insufficient for optimal selection.
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 the full burden of behavioral disclosure. It states the action ('Delete') and return values, but lacks critical details: it doesn't specify if deletion is permanent/reversible, mention authentication or permission requirements, indicate side effects (e.g., cascading deletions), or note rate limits. For a destructive tool, this leaves significant gaps in understanding its 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?
The description is well-structured and front-loaded with the core purpose. It uses three concise sentences (purpose, args, returns) with zero wasted words. Each sentence earns its place by adding distinct value: action, parameter meaning, and outcome.
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 tool's complexity (destructive operation with no annotations and no output schema), the description is incomplete. It lacks context on safety (e.g., confirmation prompts, irreversible effects), error handling beyond 'not found', and integration with sibling tools. The return value explanation is minimal, but without an output schema, more detail on responses would be helpful.
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 schema description coverage is 0%, so the description must compensate. It adds essential semantics: it clarifies that 'error_id' is a 'UUID of the error record', which is not in the schema (which only lists 'Error Id' as the title). This provides meaningful context beyond the schema, though it doesn't elaborate on UUID format or sourcing.
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 ('Delete') and resource ('an error record'), making the purpose unambiguous. It distinguishes itself from siblings like 'get_error_by_id' (read) and 'track_error' (create). However, it doesn't explicitly differentiate from potential destructive siblings beyond the verb, so it's not a perfect 5.
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_errors' or 'find_similar_errors'. It doesn't mention prerequisites (e.g., needing the error_id from another operation) or warn against misuse. The only implied usage is when you have an error_id to delete, but this is basic and insufficient for a destructive operation.
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 for behavioral disclosure. It states the tool creates a record ('track an error'), implying a write operation, but doesn't mention permissions needed, whether the operation is idempotent, rate limits, or what happens on failure. The return statement is minimal ('The created error record') without format details.
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 (purpose, args, returns) and uses bullet-like formatting for parameters. Every sentence earns its place by explaining functionality or parameters. It could be slightly more concise in the parameter explanations but remains efficient overall.
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 10-parameter creation tool with no annotations and no output schema, the description provides good parameter documentation but lacks behavioral context. It explains what data to provide but not how the tool behaves operationally (e.g., error handling, authentication). The return value is mentioned but not described in detail.
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 and 10 parameters, the description provides comprehensive parameter semantics beyond the schema. It clearly explains each parameter's purpose with examples (e.g., 'error_type: Type of error (e.g., ImportError, TypeError)'), adding significant value that 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's purpose with a specific verb ('track') and resource ('error and its solution in the knowledge base'). It distinguishes from sibling tools like 'delete_error', 'find_similar_errors', and 'search_errors' by focusing on creation rather than deletion, retrieval, or search operations.
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_errors' or 'find_similar_errors'. It mentions no prerequisites, exclusions, or specific contexts for usage, leaving the agent to infer when this creation tool is appropriate versus other operations.
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 states the tool retrieves an error record or returns None if not found, which clarifies the read-only nature and potential outcomes. However, it lacks details on permissions, rate limits, or error handling beyond the basic return behavior, leaving gaps in transparency.
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 front-loaded with the core purpose, followed by structured sections for Args and Returns, making it highly efficient and easy to parse. Every sentence adds value without redundancy, adhering to best practices for conciseness.
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?
Given the tool's low complexity (one parameter, no output schema, no annotations), the description is adequate but has clear gaps. It covers the basic operation and parameter semantics but lacks details on behavioral aspects like authentication or error scenarios. Without annotations or output schema, more context would improve completeness.
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 schema description coverage is 0%, so the description must compensate. It adds meaningful context by specifying that 'error_id' is a UUID, which clarifies the parameter's format beyond the schema's generic string type. Since there's only one parameter, this is sufficient to elevate the score above the baseline.
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 with a specific verb ('Get') and resource ('an error record by its ID'), making it immediately understandable. It distinguishes this from siblings like 'search_errors' or 'find_similar_errors' by focusing on direct ID-based retrieval rather than search operations. However, it doesn't explicitly contrast with 'delete_error' or 'track_error', keeping it from a perfect score.
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 when you have a specific error ID, as opposed to searching or finding similar errors, but it doesn't provide explicit guidance on when to use this tool versus alternatives like 'delete_error' or 'track_error'. There's no mention of prerequisites or exclusions, leaving some ambiguity in context.
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:

[![tribal_mcp_server MCP server](https://glama.ai/mcp/servers/agentience/tribal_mcp_server/badges/card.svg)](https://glama.ai/mcp/servers/agentience/tribal_mcp_server)

Score Badge

Copy to your README.md:

[![tribal_mcp_server MCP server](https://glama.ai/mcp/servers/agentience/tribal_mcp_server/badges/score.svg)](https://glama.ai/mcp/servers/agentience/tribal_mcp_server)

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/agentience/tribal_mcp_server'

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