Provides access to a community knowledge base of 16k+ error solutions and 223+ reusable skills, described as 'Stack Overflow for AI agents', enabling search, contribution, and success tracking of debugging solutions.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Hivemind MCP search_kb("ECONNREFUSED 127.0.0.1:5432")"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP server for collective debugging knowledge + project-specific knowledge bases.
Hivemind provides two knowledge layers:
Error fixes and troubleshooting from the community
223+ reusable skills and workflows
Success-ranked solutions that improve over time
Think Stack Overflow for AI agents
Auto-scans your project on setup
Builds foundational knowledge (tech stack, architecture, database, build system)
Stores project-specific solutions as you work
Cloud storage: syncs everywhere + 10x rate limits (1000/hour)
Local storage: stays private on your machine (100/hour)
Related MCP server: doc-bot
Public KB:
AI hits error → Search hivemind → Get ranked solutions → Report outcomeProject KB (Hive):
"create a new hive" → Auto-scan project → Store solutions as you work → Search your private knowledgeWhen you solve a problem, it's automatically added to your project's hive. Next session, Claude already knows how your project works.
npm install hivemind-mcpclaude mcp add hivemind -- npx hivemind-mcp@latestRestart Claude Code to load the tools.
Add to your MCP config:
{
"mcpServers": {
"hivemind": {
"command": "npx",
"args": ["hivemind-mcp@latest"]
}
}
}Tell Claude:
"create a new hive"Claude will:
Ask if you want cloud or local storage
Auto-scan your project (tech stack, architecture, database)
Create 5 foundational knowledge entries
Give you a user_id (save this!)
That's it. Now as you work, solutions get stored in your project's hive automatically.
No setup needed. Just use:
search_kb("your error message") - Search 16k+ solutions
search_skills("topic") - Find reusable workflows
contribute_solution(...) - Share what you learned
search_kb(query)
Search 16k+ error solutions and fixes.
search_kb("Cannot find module 'express'")
// Returns: npm install express (92% success rate)search_skills(query, max_results?)
Search 223+ reusable skills and workflows.
search_skills("deployment")
// Returns: Top 20 deployment-related skillsget_skill(skill_id)
Load full details of a specific skill.
get_skill(19417)
// Returns: Complete skill instructionscount_skills()
Get total number of skills in database.
count_skills()
// Returns: { total: 223 }contribute_solution(query, solution, category?)
Share a fix you discovered with the community.
contribute_solution(
"ECONNREFUSED 127.0.0.1:5432",
"Start PostgreSQL: brew services start postgresql",
"database"
)report_outcome(solution_id, outcome)
Report if a solution worked. Improves rankings.
report_outcome(123, "success") // or "failure"init_hive(project_id, project_name, storage_choice?, project_path?)
Initialize your project's knowledge base with auto-scanning.
// Step 1: Get options
init_hive("my-app", "My App")
// Returns: storage options (cloud vs local)
// Step 2: Initialize with choice
init_hive("my-app", "My App", "cloud", "/path/to/project")
// Returns: user_id + confirmation (scans project automatically)contribute_project(user_id, project_id, query, solution, category?, is_public?)
Add knowledge to your project hive.
contribute_project(
"your-user-id",
"my-app",
"How to deploy this project?",
"Run: npm run build && npm run deploy",
"deployment",
false // private
)search_project(user_id, query, project_id?, include_public?)
Search your project's knowledge base.
search_project(
"your-user-id",
"database schema",
"my-app"
)
// Returns: Your project-specific knowledge✅ 16k+ community solutions - Ranked by success rate ✅ 223+ reusable skills - Workflows and procedures ✅ Auto-scanning - Detects tech stack, architecture, database on setup ✅ Cloud sync - 10x rate limits (1000/hour) + access everywhere ✅ Private by default - Your project knowledge stays yours ✅ FTS search - Fast full-text search across solutions ✅ Success tracking - Solutions improve based on feedback
MIT
Add knowledge to project hive. TRIGGERS: 'add to hive', 'update hive', 'contribute to hive', 'store in hive'. When user says 'update hive', analyze recent work and contribute automatically. When user says 'add to hive', ask what they want to store. Stores solutions, patterns, pitfalls, architecture decisions, etc. Private by default, optionally public. Categories are dynamic - user can create any category name.
| Name | Required | Description | Default |
|---|---|---|---|
| user_id | No | Optional: User ID (auto-detected from .user_id in cwd if not provided) | |
| project_id | Yes | Project identifier | |
| query | Yes | Error message or problem description | |
| solution | Yes | What fixed it | |
| category | No | Optional category (auto-detected if not provided) | |
| is_public | No | Make this entry public (default: false/private) | |
| project_path | No | Optional: Project directory path (required for local storage) |
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 describes the tool's behavior well for different triggers, mentions privacy defaults, and explains dynamic categories. However, it doesn't cover important aspects like error handling, response format, whether this is a write operation (implied but not stated), or any rate limits or authentication requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded with the core purpose. It efficiently covers usage scenarios, storage content types, and privacy settings in a few sentences. While slightly dense, every sentence adds value and there's minimal redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a write operation with 7 parameters and no annotations or output schema, the description provides good usage context but lacks important details. It doesn't clarify what happens after contribution (success/failure indicators), doesn't mention potential side effects, and while it describes what can be stored, it doesn't explain the relationship between 'query' and 'solution' parameters. The description is adequate but has clear gaps for a mutation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 all 7 parameters thoroughly. The description doesn't add any meaningful parameter semantics beyond what's in the schema - it mentions categories are dynamic but doesn't explain how this relates to the 'category' parameter. The baseline of 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Add knowledge to project hive' with specific examples of what can be stored (solutions, patterns, pitfalls, architecture decisions). It distinguishes from siblings like 'delete_hive' or 'search_kb' by focusing on contribution rather than retrieval or deletion. However, it doesn't explicitly differentiate from 'contribute_solution' or 'update_project_entry' which appear to be similar operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidelines with trigger phrases ('add to hive', 'update hive', etc.) and different behaviors for each trigger. It specifies when to ask for user input versus automatic analysis, and mentions privacy settings (private by default, optionally public). This gives clear context for when and how to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Submit a new solution to the hivemind knowledge base.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The error message or problem this solution solves. | |
| solution | Yes | The solution that worked. | |
| category | No | Category: mcp-troubleshooting, web-automation, security, etc. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states 'Submit a new solution' but lacks details on behavioral traits such as permissions required, whether this is a write operation, potential side effects, or response format. This leaves significant gaps for a mutation 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, making it easy to understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool involves mutation (submitting new content) with no annotations and no output schema, the description is incomplete. It does not address key aspects like success indicators, error handling, or integration with the knowledge base, leaving the agent with insufficient context for reliable use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 all three parameters. The description does not add any meaning beyond the schema, such as examples or constraints not captured in the property descriptions. Baseline 3 is appropriate when the schema handles the parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Submit') and resource ('new solution to the hivemind knowledge base'), making the purpose evident. However, it does not explicitly differentiate from sibling tools like 'search_kb' or 'update_project_entry', which could involve similar knowledge base interactions, so it misses full sibling distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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. With siblings like 'search_kb' for retrieval or 'update_project_entry' for modifications, there is no indication of prerequisites, exclusions, or context for choosing this submission tool over others.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get total count of skills in the database.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states this is a read operation ('Get'), but doesn't disclose behavioral traits like performance characteristics, error conditions, or whether it requires authentication. For a database query tool with zero annotation coverage, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that states exactly what the tool does with zero wasted words. It's appropriately sized for a simple counting tool and front-loads the essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple counting tool with no parameters and no output schema, the description is minimally adequate. However, without annotations or output schema, it should ideally provide more context about what 'skills' means in this domain or what format the count returns (e.g., integer, JSON object).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so the schema already fully documents the parameter situation. The description doesn't need to add parameter information, and it correctly doesn't mention any parameters. Baseline for 0 parameters is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get' and resource 'total count of skills in the database', making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'search_skills' or 'get_skill', which might also return skill-related information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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. With siblings like 'search_skills' (which likely returns detailed skill data) and 'get_skill' (which retrieves a specific skill), there's no indication of when a simple count is preferred over more detailed queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Delete project hive and all associated knowledge entries. Use this to start fresh or remove a project's knowledge base completely.
| Name | Required | Description | Default |
|---|---|---|---|
| user_id | No | Optional: User ID (auto-detected from .user_id in cwd if not provided) | |
| project_id | Yes | Project identifier to delete | |
| project_path | No | Optional: Project directory path (required for local storage) |
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 clearly indicates this is a destructive operation ('delete', 'remove completely'), which is critical context. However, it lacks details on permissions, reversibility, confirmation prompts, or error handling, leaving gaps for a tool with such significant impact.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise (two sentences) and front-loaded with the core purpose. Every sentence earns its place: the first states the action and scope, the second provides usage context. There is zero waste or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's high complexity (destructive deletion) and lack of annotations or output schema, the description is minimally adequate. It covers the purpose and high-level usage but misses critical behavioral details like side effects, error conditions, or return values. For a tool with such permanent consequences, more completeness would be expected.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 all three parameters thoroughly. The description does not add any additional meaning or context about the parameters beyond what the schema provides, such as explaining interactions between 'user_id' and 'project_path'. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Delete project hive and all associated knowledge entries') and distinguishes it from siblings like 'init_hive' or 'get_hive_overview' by emphasizing complete removal. It uses precise verbs ('delete', 'remove') and specifies the resource ('project hive', 'knowledge base').
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit context for when to use this tool ('to start fresh or remove a project's knowledge base completely'), which helps differentiate it from alternatives like 'update_project_entry' or 'search_project'. However, it does not explicitly state when NOT to use it or mention specific prerequisites beyond the parameters.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get overview of project hive including total entries, category breakdown, and recent additions. Use when user says 'show me my hive' or 'hive overview'.
| Name | Required | Description | Default |
|---|---|---|---|
| user_id | No | Optional: User ID (auto-detected from .user_id in cwd if not provided) | |
| project_id | Yes | Project identifier | |
| project_path | No | Optional: Project directory path (required for local storage) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It describes what the tool returns but doesn't mention critical behavioral traits like whether it's read-only, requires authentication, has rate limits, or how it handles errors. For a tool with no annotations, this leaves significant gaps in understanding its operational characteristics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise and front-loaded, with two sentences that efficiently convey purpose and usage without any wasted words. Every sentence earns its place by providing essential information for tool selection and invocation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description covers purpose and usage well but lacks behavioral details and output information. It's adequate for basic selection but incomplete for full operational understanding, especially without annotations to fill gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 all three parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema, such as explaining interactions between parameters or providing examples. This meets the baseline for high schema coverage but doesn't enhance understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 specific verbs ('Get overview') and resources ('project hive'), and lists what information is included ('total entries, category breakdown, and recent additions'). However, it doesn't explicitly differentiate this from sibling tools like 'list_my_hives' or 'search_project', 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.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage triggers with natural language examples ('show me my hive' or 'hive overview'), which helps an agent know when to invoke this tool. However, it doesn't mention when NOT to use it or suggest alternatives among the many sibling tools, such as 'list_my_hives' for a different type of listing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get detailed information about a specific skill including full instructions and executable steps.
| Name | Required | Description | Default |
|---|---|---|---|
| skill_id | Yes | The ID of the skill to retrieve |
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 information, implying a read-only operation, but lacks details on permissions, rate limits, error handling, or response format. This is a significant gap for a tool with no structured annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core action ('Get detailed information') and specifies the resource and details without any wasted words. Every part of the sentence contributes directly to understanding the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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 incomplete. It covers the basic purpose but lacks behavioral context and usage guidelines, which are needed for full agent understanding in the absence of annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage, fully describing the single parameter 'skill_id' as 'The ID of the skill to retrieve'. The description adds no additional meaning beyond this, such as format examples or constraints, so it meets the baseline score when schema coverage is high.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 specific verbs ('Get detailed information') and resource ('about a specific skill'), including what information is retrieved ('full instructions and executable steps'). However, it doesn't explicitly differentiate from sibling tools like 'search_skills' or 'count_skills', 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.
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_skills' or 'list_my_hives'. It mentions retrieving a specific skill but doesn't clarify prerequisites, such as needing a skill_id, or exclusions, like when to use other tools for broader queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a new project hive (knowledge base). TRIGGERS: 'create a new hive', 'start a hive', 'initialize hive'. Onboarding flow: First call checks if user ever used Hivemind before. If first time, asks 'Is this your first time using Claude Code?' If yes, creates CLAUDE.md with starter config. Then guides through storage choice (cloud/local). If no project_path provided, creates empty hive with starter categories. IMPORTANT: Display the 'message' field to the user EXACTLY as returned - do not condense, reformat, or summarize it.
| Name | Required | Description | Default |
|---|---|---|---|
| project_id | Yes | Project identifier (e.g., from package.json name or directory) | |
| project_name | Yes | Human-readable project name | |
| is_first_time_user | No | Answer to 'Is this your first time using Claude Code?' (only used when onboarding flag not set) | |
| storage_choice | No | User's storage choice (omit on first call to get options) | |
| project_path | No | Optional: Absolute path to project directory (for scanning). If not provided, creates empty hive with starter categories only. |
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 effectively describes the tool's behavior: it triggers an onboarding flow, checks user history, creates files (CLAUDE.md), guides storage choices, and emphasizes displaying the 'message' field exactly as returned. However, it lacks details on error handling or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose but includes verbose onboarding details that may not all be essential for tool selection. Sentences like 'First call checks if user ever used Hivemind before' add value but could be more concise. Overall, it's adequately structured but slightly wordy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 5 parameters, no annotations, and no output schema, the description provides good context: it explains the onboarding flow, parameter implications, and output handling ('Display the message field exactly'). It could improve by detailing error cases or return formats, but it's largely complete for agent usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 all parameters. The description adds context for 'project_path' (creates empty hive if not provided) and 'is_first_time_user' (used when onboarding flag not set), but does not significantly enhance semantics beyond the schema's descriptions. Baseline 3 is appropriate given high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool creates a new project hive (knowledge base) with specific verb+resource. It distinguishes from siblings like 'init_project_kb' by detailing an onboarding flow with Claude Code integration and storage choices, making its purpose specific and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool, including trigger phrases ('create a new hive', 'start a hive', 'initialize hive'), onboarding flow details, and conditions for parameter usage (e.g., 'If no project_path provided, creates empty hive'). It implicitly contrasts with siblings like 'init_project_kb' by focusing on hive creation with user onboarding.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Initialize a project-specific knowledge base with cloud storage. Returns user_id to store for future contributions. Cloud storage users get 10x rate limits (1000/hour vs 100/hour).
| Name | Required | Description | Default |
|---|---|---|---|
| project_id | Yes | Unique project identifier (e.g., 'hivemind-mcp', 'my-app') | |
| project_name | Yes | Human-readable project name | |
| storage_type | No | Storage type: 'cloud' (10x limits) or 'local' (default limits) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and effectively discloses key behavioral traits: it returns a user_id for future use, specifies rate limits (1000/hour for cloud vs. 100/hour for local), and hints at initialization effects. However, it lacks details on permissions, error handling, or long-term implications.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and front-loaded, with two sentences that efficiently cover purpose, return value, and rate limits without any wasted words. Every sentence adds critical information, making it easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (initialization with storage options) and no annotations or output schema, the description is moderately complete: it covers purpose, return value, and rate limits, but lacks details on error cases, authentication needs, or what the 'user_id' entails, leaving some gaps for an agent to infer.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 all parameters well. The description adds minimal value beyond the schema by mentioning 'cloud storage' and rate limits related to 'storage_type', but it does not explain parameter interactions or provide additional semantics for 'project_id' or 'project_name'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Initialize a project-specific knowledge base with cloud storage') and the resource ('knowledge base'), distinguishing it from siblings like 'init_hive' or 'search_kb'. It specifies the outcome ('Returns user_id to store for future contributions'), making the purpose explicit and distinct.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool (to set up a knowledge base with storage options) and implies usage by mentioning future contributions, but it does not explicitly state when not to use it or name alternatives among siblings like 'init_hive' or 'search_kb' for different scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all project hives for a user. TRIGGERS: 'show me my hives', 'list my hives', 'what hives do I have', 'all my hives'. Returns project_id, project_name, and entry count for each hive. For local storage, searches current directory for .user_id files.
| Name | Required | Description | Default |
|---|---|---|---|
| user_id | Yes | User ID to list hives for | |
| project_path | No | Optional: Project directory path (for local storage) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool returns specific fields (project_id, name, entry count) and mentions local storage behavior (searching for .user_id files), which adds useful context. However, it doesn't cover permissions, rate limits, or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded with the core purpose. The trigger examples and local storage note add value without redundancy. However, the trigger list could be slightly condensed.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read-only list tool with no annotations and no output schema, the description adequately covers purpose and basic behavior. It mentions return fields and local storage context, but lacks details on pagination, error cases, or response format, leaving some gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 parameters. The description adds marginal value by mentioning 'for local storage' in relation to project_path, but doesn't provide additional syntax or format details beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('List all project hives for a user') and resource ('project hives'), distinguishing it from siblings like 'get_hive_overview' (detailed view) or 'delete_hive' (destructive action). It also specifies the scope ('for a user') and output fields.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool (e.g., triggered by phrases like 'show me my hives'), but doesn't explicitly state when not to use it or name alternatives among siblings. It implies usage for listing user hives versus other hive-related operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Report whether a solution worked or not. Helps improve solution rankings.
| Name | Required | Description | Default |
|---|---|---|---|
| solution_id | No | The ID of the solution from search results. | |
| outcome | Yes | Did the solution work? |
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 reports outcomes to improve rankings, implying a write/mutation operation, but doesn't disclose behavioral traits such as permissions required, whether the action is reversible, rate limits, or what happens after reporting (e.g., confirmation). For a mutation tool with zero annotation coverage, this is a significant gap 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and front-loaded: two sentences with zero waste. The first sentence states the purpose, and the second adds context ('Helps improve solution rankings'). Every sentence earns its place, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (mutation with 2 parameters, no annotations, no output schema), the description is incomplete. It lacks details on behavioral aspects (e.g., what 'improve solution rankings' entails, confirmation of action), and while the schema covers parameters, the overall context for proper usage is insufficient. A mutation tool should provide more guidance on effects and requirements.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with clear descriptions for both parameters (solution_id and outcome with enum). The description adds no additional meaning beyond the schema, such as explaining how solution_id is obtained or the impact of reporting outcomes. Baseline 3 is appropriate since the schema does the heavy lifting, but no extra value is provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Report whether a solution worked or not' specifies the verb (report) and resource (solution outcome). It distinguishes from siblings like search_kb or get_skill by focusing on feedback rather than retrieval. However, it doesn't explicitly differentiate from contribute_solution, which might also involve solution-related actions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides minimal guidance: 'Helps improve solution rankings' implies usage for ranking purposes, but it doesn't specify when to use this tool versus alternatives like contribute_solution or update_project_entry, nor does it mention prerequisites (e.g., needing a solution_id from search results). No explicit when/when-not or alternative tools are named.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search the hivemind knowledge base for troubleshooting solutions, error fixes, and best practices. Returns ranked solutions with success rates.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Error message, problem description, or technology to search for. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses that results are 'ranked solutions with success rates', which adds useful behavioral context about output format and ranking. However, it doesn't cover important aspects like pagination, rate limits, authentication requirements, or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with zero waste. The first sentence establishes purpose and scope, the second describes the return format. Every word earns its place in this efficiently structured description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with no annotations and no output schema, the description provides adequate basic information about purpose and return format. However, it lacks details about result structure, error conditions, or performance characteristics that would be helpful given the absence of structured metadata.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single 'query' parameter, so the schema already documents it adequately. The description doesn't add any parameter-specific information beyond what's in the schema, maintaining the baseline score for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search'), the target resource ('hivemind knowledge base'), and the content scope ('troubleshooting solutions, error fixes, and best practices'). It distinguishes from siblings like 'search_project' and 'search_skills' by specifying the knowledge base domain.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for troubleshooting and error resolution contexts, but provides no explicit guidance on when to use this tool versus alternatives like 'search_project' or 'search_skills'. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search project hive for knowledge. TRIGGERS: 'search my hive for [topic]', 'search hive [query]', 'find in hive [topic]', 'what does my hive know about [topic]'. Searches your private entries + optionally public entries. Returns relevant solutions, patterns, architecture decisions, etc.
| Name | Required | Description | Default |
|---|---|---|---|
| user_id | No | Optional: User ID (auto-detected from .user_id in cwd if not provided) | |
| query | Yes | Search query | |
| project_id | No | Optional: limit to specific project | |
| include_public | No | Include public entries in results (default: true) | |
| project_path | No | Optional: Project directory path (required for local storage) |
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 mentions that searches include private entries and optionally public ones, and describes the type of content returned. However, it doesn't cover important aspects like whether this is a read-only operation, what permissions are needed, how results are formatted/paginated, or any rate limits—leaving significant gaps for a search tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is reasonably concise with two sentences and example triggers. The first sentence states the purpose clearly, and the second adds useful scope and return information. While the trigger examples are helpful, they could be more integrated into the main description rather than listed separately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with 5 parameters, no annotations, and no output schema, the description provides adequate basic information about purpose and scope. However, it lacks details about the return format, error conditions, authentication needs, or how it differs from sibling search tools—making it incomplete for optimal agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 all 5 parameters thoroughly. The description adds minimal value beyond the schema—it implies the 'query' parameter is central and mentions public/private scope, but doesn't provide additional syntax, format, or usage details for parameters. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches a 'project hive for knowledge' and returns specific content types like solutions and architecture decisions, which is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'search_kb' or 'search_skills', leaving some ambiguity about when to use this particular search tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides example triggers that imply usage for searching personal knowledge bases, and mentions searching 'private entries + optionally public entries', giving some context. However, it lacks explicit guidance on when to use this tool versus alternatives like 'search_kb' or 'search_skills', and doesn't mention any exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for skills by topic/keyword. Returns lightweight summaries - use get_skill() for full details.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Topic or keyword to search for (e.g., 'deployment', 'testing', 'CI/CD') |
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 format ('lightweight summaries') which is valuable context beyond the input schema. However, it doesn't address important behavioral aspects like pagination, rate limits, authentication requirements, error conditions, or what constitutes a 'lightweight summary' versus full details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise and well-structured in just two sentences. The first sentence states the core purpose, the second provides crucial usage guidance. Every word earns its place with zero waste or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with no annotations and no output schema, the description provides adequate but incomplete context. It covers the basic purpose and distinguishes from one sibling, but doesn't explain the return format in detail, error handling, or how results are structured. Given the complexity of search operations and lack of output schema, more completeness would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema already fully documents the single 'query' parameter. The description adds no additional parameter semantics beyond what's in the schema. The baseline score of 3 is appropriate when the schema does all the parameter documentation work.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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 skills by topic/keyword' specifies both the verb (search) and resource (skills). It distinguishes from sibling 'get_skill' by mentioning it returns 'lightweight summaries' versus full details. However, it doesn't explicitly differentiate from other search siblings like 'search_kb' or 'search_project'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool versus an alternative: 'use get_skill() for full details' explicitly names the alternative tool for detailed information. However, it doesn't specify when NOT to use this tool or mention other potential alternatives among siblings like 'search_kb' or 'search_project'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Update an existing project hive entry. Can edit query, solution, or category. Only works for project entries (not global hivemind KB).
| Name | Required | Description | Default |
|---|---|---|---|
| user_id | No | Optional: User ID (auto-detected from .user_id in cwd if not provided) | |
| entry_id | Yes | ID of the entry to update (from search results) | |
| query | No | Optional: New query text | |
| solution | No | Optional: New solution text | |
| category | No | Optional: New category name | |
| project_path | No | Optional: Project directory path (required for local storage) |
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 it's an update operation, implying mutation, but doesn't cover critical aspects like required permissions, whether changes are reversible, error handling, or response format. The description adds minimal context beyond the basic action, leaving significant gaps for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two sentences that front-load the core action and scope. The first sentence clearly states the purpose, and the second adds important contextual limitation. There's no wasted text, though it could be slightly more structured (e.g., by explicitly listing parameters).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (mutation with 6 parameters), lack of annotations, and no output schema, the description is moderately complete. It covers the basic purpose and scope but misses behavioral details like permissions, reversibility, and response format. The schema provides good parameter documentation, but the description doesn't fully compensate for the missing behavioral context, making it adequate but with clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 all 6 parameters thoroughly. The description mentions that it can edit 'query, solution, or category,' which maps to three parameters, but doesn't add meaningful semantics beyond what the schema provides (e.g., it doesn't explain interactions between parameters or provide usage examples). Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Update') and resource ('existing project hive entry'), specifying what can be edited (query, solution, or category). It distinguishes this tool from potential global KB operations by stating 'Only works for project entries (not global hivemind KB).' However, it doesn't explicitly differentiate from sibling tools like 'contribute_project' or 'delete_hive' beyond the scope limitation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides some context by specifying that it only works for project entries, not global KB, which helps narrow usage. However, it doesn't explicitly state when to use this tool versus alternatives like 'contribute_project' (which might create entries) or 'delete_hive' (which might remove them), nor does it mention prerequisites or exclusions beyond the project scope.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
contribute_projectcontribute_solutioncount_skillsdelete_hiveget_hive_overviewget_skillinit_hiveinit_project_kblist_my_hivesreport_outcomesearch_kbsearch_projectsearch_skillsupdate_project_entryThere is significant overlap between tools, particularly contribute_project and contribute_solution both adding knowledge, and search_kb, search_project, and search_skills all performing searches with unclear boundaries. However, descriptions provide some differentiation, such as project-specific vs. global scope and entry types.
Most tools follow a consistent verb_noun pattern (e.g., contribute_project, get_hive_overview, search_kb), with minor deviations like init_hive vs. init_project_kb and list_my_hives using 'my' inconsistently. Overall, naming is readable and predictable.
With 14 tools, the count is well-scoped for managing project hives and a global knowledge base, covering creation, contribution, search, and deletion. Each tool appears to serve a distinct operational need without being excessive.
The toolset provides good coverage for hive lifecycle (init, contribute, search, delete) and knowledge management, with minor gaps such as no explicit tool for listing all entries in a hive or managing user settings. Core workflows are supported, allowing agents to perform most expected tasks.
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If you are the server author, to access and configure the admin panel.
Curated knowledge API for AI agents - skill packs, semantic search, validated patterns.
AI-verified knowledge base with trust scoring, temporal facts, and skill cards.
Team knowledge base for snippets, API calls and docs. Agents search and file items; humans review.
Structured knowledge base for AI agent solutions. Search, explore, and retrieve build logs.
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