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 "Deploy 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 responsibility for disclosing side effects, permissions, or success/failure behavior. It only says 'Submit' without mentioning whether the submission is additive, overwrites existing entries, requires authentication, or what the response contains.
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 sentence with no wasted words, front-loading the action and object.
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?
Despite the schema being complete, the description lacks any usage guidance or behavioral context. For a mutation tool with no annotations and no output schema, this is insufficient for an agent to know when to call it and what to expect.
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 already describes all three parameters (query, category, solution) with 100% coverage, so the description adds no additional semantic meaning beyond what the schema provides. Baseline score of 3 applies.
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 uses a specific verb ('Submit') and identifies the resource ('new solution to the hivemind knowledge base'), clearly distinguishing it from sibling tools like search_kb and contribute_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?
No guidance is provided on when to use this tool versus alternatives such as search_kb or report_outcome. The description only states the action without context or exclusions.
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?
With no annotations provided, the description carries the full burden of disclosing behavior. It states a simple read operation ('Get total count'), which is inherently non-destructive, but does not disclose any additional behavioral traits such as performance implications, whether the count includes all skills globally, or how the result is returned. The description is minimal but sufficient for a trivially simple operation.
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 sentence that is direct and front-loaded, containing no wasted words. It communicates the essential purpose without any filler.
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 no parameters, no output schema, and a simple count operation, the description is nearly complete. It tells the agent exactly what the tool does. However, it does not specify the exact return format (e.g., a bare integer vs. a JSON object), though this is a minor gap given the simplicity.
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 zero parameters, and the schema covers 100% of its (non-existent) properties. With no parameters to describe, the description does not need to add any parameter semantics. The baseline for zero parameters is 4, and the description does not introduce confusion.
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 ('Get') and the resource ('skills'), and specifies the result ('total count'). It is concise and unambiguous, distinguishing itself from sibling tools like search_skills and get_skill by focusing specifically on counting rather than retrieving or searching.
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. While the name implies it for counting, there is no explicit mention of when to prefer count_skills over search_skills, nor any exclusions or context. This leaves the agent to infer usage based on the name alone.
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?
No annotations are provided, so the description carries the full burden. It adds meaningful behavioral context by disclosing that the response includes 'full instructions and executable steps,' setting expectations about the return content. While it does not explicitly state that the operation is read-only, the verb 'get' and the nature of the tool make this clear.
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 sentence, front-loaded with the action and resource, and contains no unnecessary words. Every part adds value.
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 1-parameter get operation, the description adequately covers the tool's purpose and return content, especially since there is no output schema. It lacks only minor details about error handling or how to obtain the skill_id, but these are not critical for a straightforward retrieval 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?
The input schema has 100% parameter description coverage for skill_id ('The ID of the skill to retrieve'), which is straightforward. The description adds no additional parameter semantics beyond confirming the retrieval, so the baseline score of 3 applies.
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 states a specific verb ('Get') and resource ('detailed information about a specific skill'), clearly distinguishing it from sibling tools like search_skills, which implies searching across multiple skills. It also specifies the scope ('specific skill') and content ('full instructions and executable steps').
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 when a specific skill_id is known, but it does not explicitly contrast with search_skills or state when not to use this tool. No alternatives or exclusions are mentioned, so the usage guidance is only implicit.
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, the description carries the full burden and does well: it discloses the multi-step onboarding flow, conditional behavior based on project_path, and the critical instruction to display the 'message' field exactly as returned. It does not mention potential side effects beyond creating files (e.g., CLAUDE.md), but the disclosed behavior is substantial.
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 moderately detailed but well-structured with labeled sections (TRIGGERS, Onboarding flow, IMPORTANT). Every sentence adds useful information, and the critical message-handling instruction is prominently flagged. It is longer than necessary but appropriately so given the complex flow.
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?
The description covers the tool's main use case, onboarding nuances, edge cases (missing project_path), and an important output-handling instruction. However, the absence of an output schema and any mention of return values beyond the 'message' field leaves some gaps, though the given context is strong for a 5-parameter 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?
Since schema description coverage is 100%, the baseline is 3. The description adds meaningful context beyond the schema by explaining behavioral implications of omitting project_path ('creates empty hive with starter categories only'), that storage_choice should be omitted on first call, and the role of is_first_time_user. This raises it above baseline.
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 trigger phrases, making the primary purpose unambiguous. However, it does not distinguish this tool from the sibling 'init_project_kb', which likely serves a similar function, so it falls short of a 5.
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 trigger phrases and outlines the onboarding flow (first call checks user history, asks first-time question, guides storage choice), giving clear context for when to invoke the tool. It does not explicitly state when not to use it or specify alternatives, so it lacks the exclusionary guidance for a 5.
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. It discloses some behavioral traits: the tool returns a user_id and cloud storage users get 10x rate limits. However, it does not mention side effects like whether initializing an existing project overwrites data, or if special permissions are required, 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three concise sentences with no wasted words. The first sentence states the main purpose, the second instructs on the return value usage, and the third provides a key behavioral detail (rate limits). It is well-structured and front-loaded.
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 no output schema, the description appropriately explains the return value ('Returns user_id to store for future contributions'). It covers the core functionality and rate-limit consequence. However, it omits potential failure scenarios (e.g., duplicate project_id) and does not explain how the KB relates to sibling tools, so it is complete but not exhaustive.
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 coverage is 100%, so the baseline is 3. The description adds value by clarifying the storage_type parameter: 'Cloud storage users get 10x rate limits (1000/hour vs 100/hour)' explains the practical consequence of choosing cloud over local. It also notes the returned user_id should be stored for contributions, linking to project_id usage.
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 a specific verb and resource: 'Initialize a project-specific knowledge base with cloud storage.' This distinguishes it from sibling tools like search_kb or contribute_solution, and the mention of returning a user_id further clarifies its role.
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 the tool (initializing a project-specific KB) and instructs the user to store the returned user_id for future contributions. However, it does not explicitly state exclusions or alternatives, such as when to prefer init_hive instead.
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 exist, so the description carries the transparency burden. It adds the behavioral consequence 'Helps improve solution rankings,' which discloses the effect of reporting. It does not mention auth, mutability, or the optional solution_id behavior, but the core action and outcome are clear.
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 short sentences with no filler. The key action ('Report whether a solution worked') and the benefit ('improve solution rankings') are front-loaded, making it easy to scan.
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 two-parameter feedback tool, the description is adequate but omits clarification on the optional solution_id and what the tool returns or confirms. It also does not link the report explicitly to search results, though the schema partially covers this.
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 descriptions cover 100% of parameters, including the outcome enum and solution_id explanation. The tool description adds no additional parameter semantics, so the baseline of 3 applies.
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 uses the specific verb 'Report' and identifies the resource as 'a solution' with the outcome dimension. It clearly distinguishes from sibling tools like contribute_solution by focusing on feedback on existing solutions rather than adding new ones.
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 after attempting a solution ("whether a solution worked or not") and the ranking improvement suggests it is for existing solutions. However, it does not explicitly state when to use this versus contribute_solution or mention 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.
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 the full burden of behavioral disclosure. It discloses that results are 'ranked solutions with success rates,' which is beyond what the schema reveals. It does not explicitly state read-only behavior, but for a search tool this is safely inferred from 'search.'
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 two sentences, directly front-loaded with the verb 'Search,' and contains no fluff. Every word adds value, making it a model of conciseness.
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?
The tool is simple (one parameter, no output schema), and the description adequately explains the purpose and return behavior ('ranked solutions with success rates'). It could be more complete by addressing edge cases or limitations, but given the simplicity, it is sufficiently well-rounded for a basic search 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?
The schema description covers the 'query' parameter meaning fully (100% coverage), so a high parameter-semantics score is not needed. The tool description adds domain context (troubleshooting) but no additional syntax or format details, aligning with the baseline of 3.
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 a specific action ('Search') and resource ('hivemind knowledge base'), with explicit content types ('troubleshooting solutions, error fixes, and best practices'). This distinguishes it from sibling tools like 'search_skills' and 'search_project' 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 when to use the tool (when seeking troubleshooting solutions, error fixes, or best practices) and provides clear context. However, it does not explicitly mention alternatives or exclusions (e.g., 'use search_skills for skill-related queries'), so it falls short of the highest level of guidance.
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?
No annotations are provided, so the description carries the full burden. It discloses that results are 'lightweight summaries', setting expectations about the depth of the returned data and implicitly indicating that this is a non-destructive search operation. This goes beyond the schema by revealing output 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 a single concise sentence with two clauses. It front-loads the action ('Search for skills') and immediately conveys the key behavioral caveat and follow-up action. Every word serves a purpose, with no redundancy or filler.
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 one-parameter search tool with no output schema, the description adequately covers what it does, what it returns (lightweight summaries), and what to do next (use get_skill). It does not enumerate specific result fields or pagination details, but these are less critical given the explicit pointer to get_skill for full details.
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 the query parameter already clearly described as 'Topic or keyword to search for (e.g., 'deployment', 'testing', 'CI/CD')'. The description's 'by topic/keyword' adds no new meaning beyond what the schema provides, so it neither enhances nor impairs parameter 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 begins with 'Search for skills by topic/keyword', which uses the specific verb 'Search' and clearly identifies the resource ('skills'). It also distinguishes from the sibling tool get_skill by noting that this returns lightweight summaries and directing users to get_skill for full details.
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 explicitly states 'use get_skill() for full details', providing a clear alternative for a specific scenario (when full details are needed). It does not discuss alternatives like search_kb or search_project, but it gives sufficient guidance for the primary decision between search_skills and get_skill.
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.
contribute_projectcontribute_solutioncount_skillsdelete_hiveget_hive_overviewget_skillinit_hiveinit_project_kblist_my_hivesreport_outcomesearch_kbsearch_projectsearch_skillsupdate_project_entryScored across 14 tools
There 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.
Curated knowledge API for AI agents - skill packs, semantic search, validated patterns.
One searchable history across every AI coding tool, with secret scanning and a shared task board.
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.
