Auto-create Jira tickets from research insights.
Enrich Salesforce accounts with research insights.
Export insights to Signal for cross-functional sharing.
Connect to Supabase as the research repository backend.
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., "@Research Insights MCP Server What are the top 5 pain points from discovery calls this month?"
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.
Supercharge Claude Desktop with bulk analysis of 1500+ user research calls, validation workflows, and Signal export capabilities.
A Model Context Protocol (MCP) server that connects Claude Desktop to your Supabase-backed research repository, enabling AI-powered analysis of customer calls, user interviews, and support conversations at scale.
This MCP server gives Claude Desktop direct access to your user research database, enabling you to:
🔍 Search across 1500+ calls with natural language queries
📊 Aggregate insights by theme across multiple recordings
✅ Validate research findings with ML-powered confidence scoring
📈 Track trends over time (week-over-week, month-over-month)
🎯 Extract competitive intelligence from customer conversations
📦 Export to Signal for cross-functional sharing
Instead of manually reviewing hundreds of calls, ask Claude:
"What are the top 5 pain points mentioned in discovery calls this month?"
"Show me all feature requests from enterprise customers with high confidence scores"
"Which competitors are mentioned most frequently, and in what context?"
Claude can now answer these questions by directly querying your research database.
Related MCP server: Worksona MCP Server
Node.js 18+ installed
Claude Desktop (download here)
Supabase project with research data (see Database Setup)
git clone https://github.com/ecidk/mcp-research-insights.git
cd mcp-research-insights
npm install
npm run buildCopy .env.example to .env:
cp .env.example .envEdit .env with your credentials:
SUPABASE_URL=https://your-project.supabase.co
SUPABASE_SERVICE_ROLE_KEY=your-service-role-key-here
NODE_ENV=production
LOG_LEVEL=infoAdd to ~/.claude/mcp.json (create if it doesn't exist):
{
"mcpServers": {
"research-insights": {
"type": "stdio",
"command": "node /absolute/path/to/mcp-research-insights/dist/index.js",
"env": {
"SUPABASE_URL": "https://your-project.supabase.co",
"SUPABASE_SERVICE_ROLE_KEY": "${SUPABASE_SERVICE_ROLE_KEY}"
}
}
}
}💡 Tip: Use environment variable substitution (${SUPABASE_SERVICE_ROLE_KEY}) to avoid hardcoding secrets.
The MCP server will auto-start when Claude Desktop launches. Look for the 🔌 indicator showing "research-insights" is connected.
Once connected, you can have conversations like:
You: "Search for all discovery calls from the last 30 days where customers mentioned pricing concerns"
Claude: Uses search_insights_by_scope with filters: call_type=discovery, date_range=last_30_days, sentiment=negative, keywords=pricing
You: "What are the most common feature requests from enterprise customers?"
Claude: Uses analyze_feature_requests filtered by customer segment, then aggregate_insights_by_theme to cluster similar requests
You: "Show me the validation queue and predict which insights are likely to be approved"
Claude: Calls get_validation_queue then predict_validation_outcome for each item, providing confidence scores
Major expansion in v2.0: Added 51 new tools across workflow automation, advanced analytics, customer journey mapping, integrations, AI research assistance, and quality/compliance features.
Tool | Description | Use Case |
| Bulk search with complex filtering | "Find all pain points from Q1 with high confidence" |
| Retrieve curated collections | "Show me the 'Onboarding Issues' collection" |
| Filter by AI confidence scores | "Only show insights with >80% confidence" |
| Filter by validation state | "What's pending review?" |
| Full citation with timestamps | "Where did this insight come from?" |
| Recording-level search | "Find calls with [customer_name]" |
| Aggregate Sales/Support/UX | "Compare support vs sales feedback" |
Tool | Description | Use Case |
| Theme extraction across calls | "Cluster similar pain points" |
| Quality score histogram | "What's our data quality like?" |
| Period-over-period comparison | "How did feedback change month-over-month?" |
| Competitive intelligence | "Which competitors are customers evaluating?" |
| Feature request frequency | "Top 10 most requested features" |
Tool | Description | Use Case |
| Bulk approve/reject insights | "Validate these 50 insights" |
| Items pending review | "What needs review?" |
| ML confidence prediction | "Which items are likely valid?" |
| Manual override with reasoning | "Mark as invalid due to bias" |
Tool | Description | Use Case |
| Find patterns across 3+ calls | "What pain points appear in 5+ calls?" |
| Auto-generate executive summaries | "Create Q1 discovery call brief" |
| AI-powered auto-tagging | "Tag these 100 recordings by sentiment" |
| Bulk tag application | "Tag all enterprise calls" |
Tool | Description | Use Case |
| Get notified when patterns emerge | "Alert me when churn signals spike" |
| Alert on metric thresholds | "Notify when feature requests > 10/week" |
Tool | Description | Use Case |
| Tailored reports by audience | "Create exec summary for Q1" |
| Save complex filters | "Save 'Enterprise Q1 Discovery' filter" |
| Recall saved filters | "Load my enterprise filter" |
Tool | Description | Use Case |
| Compare patterns over time | "Is pricing objection increasing?" |
| Enterprise vs SMB comparison | "How do enterprise pain points differ from SMB?" |
| Cohort feedback evolution | "Track enterprise sentiment over Q1" |
| Emotion tracking in conversations | "When does frustration spike in calls?" |
| What causes reactions | "What triggers positive sentiment?" |
| Statistical outlier detection | "Why did sentiment drop last week?" |
| Root cause analysis | "Explain the churn signal spike" |
Tool | Description | Use Case |
| Link insights to journey stages | "Map Acme Corp's journey" |
| Find stages with missing feedback | "Which stages lack research?" |
| Save research snapshots | "Save Q1 onboarding research" |
| Find similar past research | "Find past pricing research" |
| Team annotations on insights | "Flag this as hypothesis to test" |
| See team notes | "Show all unresolved questions" |
Tool | Description | Use Case |
| Auto-create Jira tickets | "Create tickets for top 5 requests" |
| Send to ProductBoard | "Export validated features" |
| Add insights to Salesforce | "Enrich Acme Corp account" |
| Generate CS briefings | "Create renewal brief for Acme" |
Tool | Description | Use Case |
| AI suggests follow-up questions | "What should I ask about churn?" |
| Find what you don't know | "What's missing in onboarding research?" |
| Statistical hypothesis testing | "Test: Enterprise users want SSO more than SMB" |
| Statistical validity calculations | "How many interviews do I need?" |
Tool | Description | Use Case |
| Score research quality | "Assess quality of Q1 research" |
| Identify leading questions | "Check for bias in this recording" |
| Compliance audit trail | "Who accessed insights last month?" |
| Remove PII before sharing | "Anonymize for external share" |
Tool | Description | Use Case |
| Bulk approve/reject insights | "Validate these 50 insights" |
| Items pending review | "What needs review?" |
| ML confidence prediction | "Which items are likely valid?" |
| Manual override with reasoning | "Mark as invalid due to bias" |
Tool | Description | Use Case |
| Prepare for external sharing | "Export validated insights to Signal" |
| Record downstream usage | "Track when insights are viewed" |
This MCP server requires a Supabase project with the following tables:
-- UX analysis results
CREATE TABLE ux_analysis (
id UUID PRIMARY KEY,
recording_id UUID REFERENCES recordings(id),
call_breakdown JSONB,
question_analysis JSONB,
next_steps JSONB,
created_at TIMESTAMPTZ DEFAULT NOW()
);
-- Insight validations
CREATE TABLE ux_insight_validations (
id UUID PRIMARY KEY,
insight_id UUID,
insight_type TEXT,
validation_status TEXT, -- validated, needs_review, rejected
confidence_score FLOAT,
reason_codes TEXT[],
validated_at TIMESTAMPTZ,
created_at TIMESTAMPTZ DEFAULT NOW()
);
-- Collections (curated groups)
CREATE TABLE ux_collections (
id UUID PRIMARY KEY,
name TEXT,
description TEXT,
created_by UUID REFERENCES auth.users(id),
created_at TIMESTAMPTZ DEFAULT NOW()
);
-- Collection items (many-to-many)
CREATE TABLE ux_collection_items (
collection_id UUID REFERENCES ux_collections(id),
insight_id UUID,
added_at TIMESTAMPTZ DEFAULT NOW(),
PRIMARY KEY (collection_id, insight_id)
);
-- Recordings metadata
CREATE TABLE recordings (
id UUID PRIMARY KEY,
title TEXT,
duration INTEGER,
created_at TIMESTAMPTZ DEFAULT NOW(),
user_id UUID REFERENCES auth.users(id),
content_type TEXT -- 'user_experience', 'sales_call', 'customer_support'
);-- Validation queue view
CREATE VIEW ux_validation_queue AS
SELECT
v.id,
v.insight_id,
v.insight_type,
v.confidence_score,
v.validation_status,
v.created_at,
a.call_breakdown->>'summary' AS insight_context
FROM ux_insight_validations v
LEFT JOIN ux_analysis a ON v.insight_id = a.recording_id
WHERE v.validation_status = 'needs_review'
ORDER BY v.confidence_score ASC, v.created_at ASC;-- MCP API keys (hashed)
CREATE TABLE ux_mcp_api_keys (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
key_hash TEXT NOT NULL UNIQUE,
user_id UUID REFERENCES auth.users(id),
description TEXT,
scopes TEXT[] DEFAULT ARRAY['read'],
last_used_at TIMESTAMPTZ,
created_at TIMESTAMPTZ DEFAULT NOW()
);
-- Rate limiting function
CREATE OR REPLACE FUNCTION check_mcp_rate_limit(
p_key_hash TEXT,
p_limit INTEGER DEFAULT 100
) RETURNS BOOLEAN AS $$
-- Implementation: check if key_hash has exceeded p_limit requests in last minute
$$ LANGUAGE plpgsql;💡 See DEPLOYMENT_GUIDE.md for full schema and migration scripts.
The SUPABASE_SERVICE_ROLE_KEY bypasses Row Level Security (RLS) and grants full admin access to your database.
✅ Store in .env file (already in .gitignore)
✅ Use environment variables in production
✅ Rotate immediately if exposed
✅ Use separate keys for dev/staging/production
✅ Enable RLS on all tables (defense in depth)
✅ Monitor API usage for anomalies
❌ Commit .env to git
❌ Share keys in Slack, email, or screenshots
❌ Use production keys in development
❌ Hardcode keys in source code
❌ Expose keys in error messages or logs
Even with the service role key, enable RLS as a safety layer:
-- Example: Users can only access their own recordings
CREATE POLICY "Users access own recordings"
ON recordings FOR SELECT
USING (auth.uid() = user_id);
-- Admins can access all recordings
CREATE POLICY "Admins access all recordings"
ON recordings FOR SELECT
USING (
EXISTS (
SELECT 1 FROM user_roles
WHERE user_id = auth.uid()
AND role = 'admin'
)
);📖 Full security policy: SECURITY.md
┌─────────────────┐
│ Claude Desktop │
└────────┬────────┘
│ stdio (MCP)
↓
┌─────────────────────────────┐
│ Research & Insights MCP │
│ │
│ ┌─────────────────────┐ │
│ │ 18 Tools │ │
│ │ - Search │ │
│ │ - Analysis │ │
│ │ - Validation │ │
│ │ - Export │ │
│ └─────────────────────┘ │
│ │
│ ┌─────────────────────┐ │
│ │ Authentication │ │
│ │ - API key (SHA-256)│ │
│ │ - Rate limiting │ │
│ └─────────────────────┘ │
└─────────┬───────────────────┘
│ Supabase Client
↓
┌─────────────────────────────┐
│ Supabase Project │
│ │
│ ┌─────────────────────┐ │
│ │ PostgreSQL │ │
│ │ - ux_analysis │ │
│ │ - recordings │ │
│ │ - validations │ │
│ │ - collections │ │
│ └─────────────────────┘ │
│ │
│ ┌─────────────────────┐ │
│ │ Row Level Security │ │
│ │ (RLS Policies) │ │
│ └─────────────────────┘ │
└─────────────────────────────┘Transport: stdio (standard input/output) for Claude Desktop integration
Authentication: API keys stored as SHA-256 hashes, never plaintext
Rate Limiting: PostgreSQL function check_mcp_rate_limit() prevents abuse
Logging: Winston with configurable log levels
Error Handling: Graceful failures with user-friendly error messages
# Install MCP Inspector
npm install -g @modelcontextprotocol/inspector
# Test server
npx @modelcontextprotocol/inspector node dist/index.js# Start server in dev mode
npm run dev
# In another terminal, send test queries
echo '{"jsonrpc":"2.0","id":1,"method":"tools/list"}' | node dist/index.jsDEPLOYMENT_GUIDE.md - Production deployment, Docker, environment setup
SECURITY.md - Security policy, vulnerability reporting
SETUP_COMPLETE.md - Detailed setup walkthrough
Contributions are welcome! Please:
Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'feat: Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request
# Clone your fork
git clone https://github.com/YOUR-USERNAME/mcp-research-insights.git
cd mcp-research-insights
# Install dependencies
npm install
# Run in development mode (auto-reload)
npm run dev
# Run tests
npm test
# Build for production
npm run buildCheck ~/.claude/mcp.json syntax (valid JSON)
Verify absolute path to dist/index.js
Restart Claude Desktop completely
Check logs: ~/Library/Logs/Claude/mcp-research-insights.log
Ensure .env file exists in project root
Verify environment variables in mcp.json are correct
Check SUPABASE_URL format: https://xxx.supabase.co (no trailing slash)
Verify SUPABASE_SERVICE_ROLE_KEY (not anon/public key)
Check RLS policies allow service role access
Confirm tables exist in public schema
Default: 100 requests/minute per API key
Increase via MAX_REQUESTS_PER_MINUTE in .env
Check rate limit logs: grep "rate limit" ~/Library/Logs/Claude/mcp-research-insights.log
Aggregate feature requests across 1000+ customer calls
Track sentiment trends for specific features
Prioritize roadmap based on customer pain points
Extract themes from user interviews at scale
Validate research findings with confidence scores
Export insights for cross-functional sharing
Identify at-risk accounts from support call patterns
Track product adoption challenges
Surface competitive threats early
Analyze objection patterns in discovery calls
Understand why deals are won/lost
Competitive intelligence from customer conversations
Model Context Protocol - Official MCP documentation
Claude Desktop - Download Claude Desktop app
Supabase - Open source Firebase alternative
SoundScribe - AI-powered call analysis platform
MIT License - see LICENSE for details.
Built with:
Issues: GitHub Issues
Security: See SECURITY.md for reporting vulnerabilities
Discussions: GitHub Discussions
Made with ❤️ by ECI Software Solutions
Add contextual notes to insights
| Name | Required | Description | Default |
|---|---|---|---|
| insight_id | Yes | ||
| note_type | Yes | ||
| content | Yes | ||
| mention_users | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations and a brief description, there is no disclosure of side effects, permissions, reversibility, or other behavioral traits. The description is insufficient 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 very short and to the point, which is good for conciseness. However, it lacks any structure or detail, making it less helpful despite its brevity.
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 has 4 parameters, an enum, and no output schema, the description should cover more about return values, behavior, and constraints. It is incomplete for effective 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 0%, so the description must compensate but does not mention any parameters. The enum in the schema provides some meaning, but the description adds no additional information about parameter usage or validation.
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?
Description clearly states the verb 'Add' and resource 'notes to insights', specifying the action and target. However, it does not distinguish from sibling tools, which could create ambiguity, but the tool name itself is fairly unique among siblings.
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 on when to use this tool versus alternatives like create_insight_snapshot or add_annotation. The description provides no context for usage scenarios or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Extract and group insights by themes
| Name | Required | Description | Default |
|---|---|---|---|
| scope | No | ||
| theme_keywords | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description must disclose behavioral traits. It states 'extract and group' but does not indicate whether the operation is read-only, if it modifies data, or if it has side effects. There is no mention of required permissions, rate limits, or output behavior, making it insufficient for safe invocation.
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 short, but brevity comes at the cost of completeness. It is front-loaded with the action, but the structure is flat and lacks any detail or context. While not verbose, it does not earn its place every sentence since it omits critical 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?
Given the complexity (2 parameters including an unstructured object, no output schema, and 51 sibling tools), the description is severely incomplete. It does not explain the return value, how the grouping works, or how to construct the scope parameter. The tool cannot be used correctly without additional context.
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 0% description coverage, and the description adds no meaning to the parameters. 'scope' is an object with no structure explained, and 'theme_keywords' is an array of strings without format or purpose details beyond the vague 'by themes'. The description fails to compensate for the lack of schema documentation.
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 'Extract and group insights by themes' provides a verb and resource but is vague. It doesn't specify the source of insights (e.g., from a dataset or report) or what 'themes' means in context. Among many sibling tools with similar purposes (e.g., 'detect_recurring_patterns', 'analyze_feature_requests'), this description lacks differentiation.
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 its many alternatives. There is no mention of prerequisites, scope, or exclusion criteria. The description fails to help the agent decide between this and tools like 'search_insights_by_scope' or 'identify_knowledge_gaps'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Extract and analyze feature request frequency
| Name | Required | Description | Default |
|---|---|---|---|
| date_range | No | ||
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description lacks behavioral details beyond the basic action. There are no annotations to indicate read-only or destructive nature, and the description does not specify whether the tool modifies data, requires permissions, or has rate limits. The minimal description fails to compensate for the absence of 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 concise at one sentence, but it sacrifices informativeness. It is front-loaded with the core action but lacks structure and additional detail, making it merely adequate rather than well-crafted.
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 complexity of two parameters (including a nested object), no output schema, and a large set of sibling tools, the description is severely incomplete. It does not explain input format, expected output, or how this tool fits into the broader toolset, providing minimal context for correct 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?
With 0% schema description coverage and no parameter information in the description, the parameters are entirely opaque. The description does not mention the date_range or limit parameters, nor their meanings or usage, leaving the agent without guidance on how to correctly invoke the tool.
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 'Extract and analyze feature request frequency' clearly states the tool's function: extracting and analyzing the frequency of feature requests. It distinguishes from siblings like 'analyze_sentiment_shifts' by its specific focus on feature requests, though it could be more explicit about the resource type.
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. Among many sibling tools, there is no mention of context, prerequisites, or when this tool is preferred, leaving the agent to infer usage independently.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Track sentiment changes within conversations
| Name | Required | Description | Default |
|---|---|---|---|
| recording_ids | Yes | ||
| track_by | No | timeline | |
| emotion_categories | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description gives no information about behavioral traits such as whether it modifies data, output format, or side effects. It only states the high-level purpose.
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, making it concise, but it lacks necessary detail. It could be expanded to include key behavioral or parameter context without losing 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?
Given the tool has 3 parameters, no output schema, and no annotations, the description is incomplete. An agent would struggle to use the tool correctly without additional information.
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?
With 0% schema description coverage, the description does not explain the meaning or usage of any of the three parameters. The agent gets no help understanding recording_ids, track_by, or emotion_categories.
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 tracks sentiment changes in conversations. It is specific enough to distinguish from siblings like analyze_feature_requests or detect_recurring_patterns, but lacks detail on what 'track' entails.
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 on when to use this tool versus alternatives. No mention of when not to use it or any prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Remove PII before sharing
| Name | Required | Description | Default |
|---|---|---|---|
| insight_ids | Yes | ||
| anonymization_level | No | partial | |
| preserve_context | No |
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 disclosing behavioral traits. The description only states 'Remove PII' but does not specify whether this modifies the insights in place, returns new sanitized data, requires specific permissions, or is reversible. For a mutation-like tool, 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 sentence, making it concise. However, it is under-informative; it could be expanded to include parameter context without becoming verbose. It is not as efficient as possible because it sacrifices clarity for brevity.
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 has three parameters, no output schema, and no annotations, the description should provide more context about how the tool works, what happens to the insights, and the meaning of parameters. The current description is too sparse to guide an agent effectively.
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 0%, and the description does not explain any of the three parameters (insight_ids, anonymization_level, preserve_context). The enum values 'partial' and 'full' are not defined, and the role of preserve_context is unclear. The description adds no semantic value beyond the parameter names.
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 'Remove PII before sharing' clearly states the action (remove PII) and the resource (insights), providing a specific verb and resource. It distinguishes the tool from siblings by focusing on anonymization rather than analysis or search. However, it does not elaborate on what 'PII' encompasses, leaving some ambiguity.
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 phrase 'before sharing' implies the tool is used when sharing insights, but it provides no explicit guidance on when to use this tool versus alternatives, no warnings about when not to use it, and no clarification of conditions for partial vs full anonymization. This is minimal context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Score research quality
| Name | Required | Description | Default |
|---|---|---|---|
| recording_ids | Yes | ||
| criteria | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior, but it fails entirely. It does not state whether the tool is read-only, whether it modifies data, or any side effects. The agent gets no insight into what happens when the tool is invoked.
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 vague sentence, which is insufficient. Conciseness should pack meaning into few words, but here the brevity is due to lack of information, not efficiency.
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?
Without output schema, annotations, or parameter explanations, the description is severely incomplete. The agent cannot determine what the tool returns, how to interpret 'scores', or what constraints apply.
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 0%, so the description must explain parameters. However, it does not mention 'recording_ids' or 'criteria' at all, leaving their purpose and constraints completely undocumented.
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 'Score research quality' is a tautology that simply restates the tool's name without adding specificity. It does not clarify what 'scoring' entails or how this tool differs from siblings like 'detect_research_bias' or 'validate_insight_batch'.
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 the numerous sibling tools. The description lacks any context about prerequisites, typical use cases, or when to avoid using it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Track who accessed what insights
| Name | Required | Description | Default |
|---|---|---|---|
| date_range | Yes | ||
| user_id | No | ||
| include_exports | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral traits. It merely implies a read-like action but does not specify whether it is read-only, requires permissions, or has rate limits. No side effects or return format are mentioned.
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?
While the description is concise, it is under-specified for a 3-parameter tool with no schema descriptions. It sacrifices necessary detail for brevity, making it inadequate rather than efficient.
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 lack of annotations, output schema, and parameter descriptions, the description fails to provide enough context. The agent cannot determine what 'insights' means, how results are returned, or what filtering options do.
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 3 parameters with 0% description coverage, and the description provides no explanation for 'date_range', 'user_id', or 'include_exports.' This leaves the agent to infer meaning from names alone, which is insufficient for correct 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 states 'Track who accessed what insights,' which clearly identifies the action (track) and the resource (insights). However, it does not differentiate from sibling tools like 'track_signal_usage' or 'search_research_history,' missing an opportunity to clarify scope.
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 on when to use this tool versus alternatives such as 'track_signal_usage' or 'get_validation_queue.' The description lacks context for appropriate invocation or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
AI-powered auto-tagging with confidence scores
| Name | Required | Description | Default |
|---|---|---|---|
| recording_ids | Yes | ||
| tag_categories | No | ||
| confidence_threshold | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It mentions 'AI-powered' and 'confidence scores', but does not state whether the tool modifies recordings, overwrites existing tags, or is read-only. The mutation aspect is implied but not elaborated.
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 short (just two phrases), which is under-specification rather than conciseness. It lacks structure and does not earn its space; a few more sentences would be necessary for adequacy.
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?
With 3 parameters, no output schema, and no annotations, the description is critically incomplete. It omits return values, side effects, error conditions, and any details about the tagging process. For a tool involving AI and confidence, much more context is needed.
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 0%, meaning the description must add meaning beyond parameter names and types. It only mentions 'confidence scores' loosely related to 'confidence_threshold' but gives no explanation for 'recording_ids' or 'tag_categories'. The description adds virtually no semantic value.
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 'AI-powered auto-tagging with confidence scores', which clearly indicates the tool applies tags to recordings with AI and includes confidence scores. The verb (auto-tag) and resource (recordings) are explicit. However, it does not distinguish from sibling 'batch_apply_tags', which may also involve tagging; differentiating would make it stronger.
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?
There is no guidance on when to use this tool versus alternatives like 'batch_apply_tags' or 'search_by_confidence'. No prerequisites, scenarios, or exclusions are mentioned, leaving the agent without decision-making support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Bulk tag application to multiple recordings
| Name | Required | Description | Default |
|---|---|---|---|
| recording_ids | Yes | ||
| tags | Yes | ||
| overwrite | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description should compensate. It only states the action without mentioning behavioral traits like overwrite behavior, performance impact, or required permissions.
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?
Extremely concise (5 words), but this underspecifies the tool. Could be improved without significant length increase.
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?
Lacks details on input format (especially the 'tags' array of objects), output, and error handling. Incomplete for a bulk operation with three parameters.
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?
Description provides no information about the three parameters (recording_ids, tags, overwrite) even though schema coverage is 0%, leaving the agent to guess their structure and purpose.
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?
Description clearly states verb ('bulk tag application') and resource ('recordings'), but lacks differentiation from sibling tool auto_tag_recordings, which may confuse selection.
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 on when to use this tool versus alternatives like auto_tag_recordings, nor any prerequisites or context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate quality score histogram
| Name | Required | Description | Default |
|---|---|---|---|
| bucket_size | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose any behavioral traits (e.g., read-only, destructive, authorization needs, rate limits). It fails to add value beyond the basic action.
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, which is concise, but it is under-specified and lacks essential detail. It is not appropriately sized for the tool's complexity.
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 lack of annotations, no output schema, and only one undocumented parameter, the description is highly incomplete. It does not explain what 'quality score' means, the histogram format, or any aggregation behavior.
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 0%, but the description does not describe the parameter 'bucket_size' at all. The schema provides defaults and constraints, but no explanation of its role in generating the histogram.
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 'Generate' and a specific resource 'quality score histogram', making the purpose clear. However, it does not differentiate from sibling tools like 'assess_research_quality' which might also produce quality-related outputs.
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 on when to use this tool versus alternatives. With many sibling tools, explicit usage context is missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
How many calls needed for statistical validity
| Name | Required | Description | Default |
|---|---|---|---|
| effect_size | No | medium | |
| confidence_level | No | ||
| power | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility for behavioral disclosure. It only states the purpose, omitting details like whether the tool is read-only (it's a calculation, presumably safe), what inputs affect the calculation, or how results are returned.
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 very concise (one sentence), but it is under-specified. While brevity is valued, it lacks necessary detail about parameters and output, making it less helpful than it could be.
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 has three parameters with 0% schema coverage and no output schema. The description fails to explain the function's inputs, outputs, or assumptions, leaving the agent with insufficient information for correct invocation.
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 0%, yet the description provides no explanation of the three parameters (effect_size, confidence_level, power). It does not clarify enum options or numerical ranges, leaving the agent without meaningful semantic guidance beyond the schema.
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 'How many calls needed for statistical validity' clearly indicates the tool calculates required sample size for statistical significance. It uses a specific verb ('calculate') and resource ('sample size'), effectively distinguishing it from sibling tools like 'test_hypothesis' or 'calculate_confidence_distribution'.
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 like 'calculate_confidence_distribution' or prerequisites such as required effect size or confidence level. The description lacks any context for appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compare insights between customer segments
| Name | Required | Description | Default |
|---|---|---|---|
| cohort_a | Yes | ||
| cohort_b | Yes | ||
| metrics | No | ||
| date_range | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits, but it only offers a vague statement. It does not indicate side effects, permissions, output format, or any constraints, making it insufficient for safe tool use.
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 brief, but brevity here sacrifices informative content. While it is front-loaded, it does not earn its place with valuable details, resulting in under-specification.
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 (four parameters, nested objects, no output schema), the description is woefully incomplete. It lacks any indication of expected input structures, metric options, or return values.
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 0%, yet the description provides no additional meaning for the four parameters. The meaning of cohort_a, cohort_b, metrics, and date_range is left entirely unspecified.
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 'compare' and the resource 'insights between customer segments', which conveys the basic purpose. However, it does not differentiate from sibling tools like 'track_cohort_over_time' or other analysis tools, lacking specificity.
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. There is no mention of preferred use cases, prerequisites, or exclusions, leaving the agent without decision context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate CS briefing before renewal calls
| Name | Required | Description | Default |
|---|---|---|---|
| customer_id | Yes | ||
| briefing_type | Yes | ||
| include_sections | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. It only states 'generate,' implying creation but lacks detail on side effects (e.g., does it store the briefing?), permissions required, or limitations. This is insufficient for an agent to understand the 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 short (5 words), which is concise but at the expense of completeness. It is front-loaded with key context, but the brevity forces the agent to guess too much. A few more words would improve it without losing 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?
Given the tool has 3 parameters, no output schema, and no annotations, the description is too sparse. It does not explain the output format, default behavior for sections, or any constraints on briefing_type. The agent lacks enough context to use the tool correctly.
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?
With 0% schema description coverage, the description must explain parameters. It does not mention 'customer_id', 'briefing_type', or 'include_sections' at all, leaving the agent to infer from parameter names alone. This is a critical gap.
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 'Generate CS briefing before renewal calls' clearly identifies the action (generate) and resource (CS briefing) and provides context (before renewal calls). It distinguishes from sibling tools like 'generate_research_brief' by specifying the CS and renewal focus, though it could be more specific about what a CS briefing entails.
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 on when to use this tool versus alternatives. Sibling tools like 'generate_research_brief' or 'create_stakeholder_report' might overlap, but the description does not clarify when to choose this one. No explicit exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Save current analysis as reusable snapshot
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes | ||
| description | Yes | ||
| include_filters | No | ||
| share_with | No | ||
| tags | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It only says 'save', implying a write operation, but omits side effects (e.g., overwriting existing snapshots), persistence details, authentication needs, or return values. The parameter 'share_with' hints at sharing but is not explained.
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 short sentence (5 words), making it concise but lacking structure. It does not front-load key details or organize information hierarchically. Given the tool's complexity (5 parameters, nested objects), the description is under-specified.
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 has 5 parameters, no output schema, and no annotations. The description fails to cover return values, success/failure indications, preconditions (e.g., must have an active analysis), or post-conditions. It is severely incomplete for an AI agent to use correctly.
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 has 0% description coverage; the description does not compensate. It does not explain any of the 5 parameters (title, description, include_filters, share_with, tags). For example, 'include_filters' is an object with unknown structure, and 'share_with' suggests user IDs but no format is given.
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: saving the current analysis as a reusable snapshot. It uses a verb ('save') and a resource ('snapshot'), distinguishing it from siblings that focus on other actions like searching or exporting. However, it does not elaborate on what a 'snapshot' entails, slightly limiting clarity.
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 usage guidelines are provided. The description does not specify when to use this tool (e.g., after completing an analysis) or when not to use it (e.g., if merely saving filters). It also fails to mention alternatives like 'save_search_filter' which might overlap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get notified when patterns emerge
| Name | Required | Description | Default |
|---|---|---|---|
| alert_name | Yes | ||
| conditions | Yes | ||
| notification_channels | No | ||
| recipients | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavioral traits. It does not mention whether the alert is persistent, whether it requires permissions, what triggers it, or any side effects. Completely opaque.
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 short sentence, but it is under-specified rather than concise. It lacks critical details and does not earn its place.
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 (nested objects, no output schema, many siblings), the description is completely inadequate. It provides virtually no context for the agent to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description adds no meaning to the four parameters (alert_name, conditions object, notification_channels, recipients). The agent gets no help understanding what 'conditions' should contain or how channels 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 'Get notified when patterns emerge' indicates the tool creates notifications for pattern alerts, but lacks a precise verb and resource. It vaguely matches the tool name but doesn't specify what kind of patterns or how alerts work, limiting clarity.
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 on when to use this tool versus alternatives like 'detect_recurring_patterns' or 'track_pattern_trends'. No conditions, prerequisites, or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tailored reports for product/exec/sales/engineering
| Name | Required | Description | Default |
|---|---|---|---|
| audience | Yes | ||
| focus_areas | No | ||
| time_period | No | last_month |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, and the description fails to disclose any behavioral traits such as output format, side effects, authorization needs, or error conditions. The agent gains no insight into what happens during report creation.
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 no fluff. However, it sacrifices substance for brevity—it is too short to be useful.
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, no annotations, and 3 undocumented parameters, the description is severely incomplete. The agent lacks critical information about report structure, output format, and how to effectively use parameters like focus_areas or time_period.
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 3 parameters (audience, focus_areas, time_period) with 0% description coverage. The description adds no information about any parameter, leaving the agent to guess their meaning and constraints beyond the schema's enum and defaults.
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 'Tailored reports for product/exec/sales/engineering' clearly indicates the tool creates stakeholder-specific reports. The verb is implied, and the resource (stakeholder reports) is evident. However, it does not explicitly differentiate from sibling tools like create_customer_briefing or generate_research_brief, though the audience enum provides some 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?
No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, context, or scenarios where this tool is preferred over sibling report-generation tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find statistically unusual patterns
| Name | Required | Description | Default |
|---|---|---|---|
| metrics | No | ||
| baseline_period | No | last_90_days | |
| sensitivity | No | medium |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the full burden of behavioral disclosure. It only states the purpose but does not mention output format, side effects (e.g., read-only), required permissions, or whether it consumes significant resources. This is insufficient for an AI agent to assess safety and behavior.
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, consisting of a single sentence. While it is front-loaded, it sacrifices necessary detail. It could be longer to cover parameters and usage without becoming verbose.
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 has 3 parameters, no output schema, and no annotations, the description is severely incomplete. It does not explain what the tool returns, how to interpret results, or how parameters affect behavior. Sibling differentiation is minimal. The description fails to provide enough context for effective tool selection and invocation.
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 0%, yet the description does not explain any of the three parameters: 'metrics', 'baseline_period', or 'sensitivity'. The agent is left to infer meaning from names and defaults, which is inadequate for a statistical detection tool. The description adds no value over the raw schema.
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 'Find statistically unusual patterns' clearly states the action (find) and the resource (statistically unusual patterns). It distinguishes from siblings like 'detect_recurring_patterns' which focuses on recurring patterns, not anomalies. However, it lacks specificity on the type of anomalies, so scores 4 instead of 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?
No guidance on when to use this tool versus alternatives such as 'monitor_kpi_thresholds' or 'explain_anomaly'. The description does not provide context for appropriate use cases or mention conditions where this tool is preferable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find patterns that appear across multiple calls (min_frequency 3+)
| Name | Required | Description | Default |
|---|---|---|---|
| min_frequency | No | ||
| pattern_types | No | ||
| timeframe | No | last_30_days | |
| confidence_threshold | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden but only reveals that it finds patterns across multiple calls with a minimum frequency. It does not disclose behavioral details like how timeframes or confidence thresholds affect results, or if the tool is read-only or has side effects.
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 single sentence is front-loaded and concise, but given the tool's complexity (4 parameters, no output schema), it is too sparse. More sentences could be added without becoming verbose.
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 4 optional parameters and no output schema, the description is inadequate. It does not explain what 'patterns' are, how results are returned, or any prerequisites. This leaves many open questions for an AI agent.
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 0%, so the description must compensate. It adds meaning for min_frequency (default 3) but ignores the other three parameters (pattern_types, timeframe, confidence_threshold), leaving their semantics undocumented.
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 finds patterns across multiple calls with a minimum frequency of 3, providing a specific verb and resource. However, it does not differentiate from siblings like detect_anomalies or aggregate_insights_by_theme, which might also identify patterns.
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 given on when to use this tool versus alternatives such as detect_anomalies or track_pattern_trends. The description only states what it does without context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Identify leading questions, confirmation bias
| Name | Required | Description | Default |
|---|---|---|---|
| recording_id | Yes | ||
| bias_types | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits such as whether the tool is read-only, side effects, or required permissions. It only states the basic function.
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?
Extremely concise (4 words), but at the expense of necessary detail. No waste, but too brief for adequate understanding.
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 and no annotations, the description is incomplete. It does not explain the return value, how the analysis works, or what the tool requires beyond parameters.
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 description adds minimal meaning beyond the parameter names. 'recording_id' and 'bias_types' are not explained; the description only mentions two bias types, but the schema defaults include three. Schema coverage is 0%, so the description should compensate but does not.
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 identifies leading questions and confirmation bias, which is a specific verb-resource combination. It partially distinguishes from siblings like 'assess_research_quality' but lacks explicit differentiation.
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 on when to use this tool versus alternatives. Does not specify context, prerequisites, or 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.
Add research insights to Salesforce account records
| Name | Required | Description | Default |
|---|---|---|---|
| account_id | Yes | ||
| insight_summary | No | ||
| recent_feedback | No | ||
| risk_signals | No | ||
| expansion_opportunities | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral traits. It only says 'Add research insights', but does not disclose side effects (e.g., modifications to Salesforce records), required permissions, rate limits, or other important behaviors.
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 (one sentence) and front-loaded with key verb and resource. However, it sacrifices necessary detail; every sentence should earn its place, and this one provides minimal value beyond the name.
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 (5 parameters, nested object, no output schema, no annotations, 47 siblings), the description is drastically insufficient. It fails to explain parameters, return values, or usage context, making it incomplete for reliable invocation.
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 5 parameters with 0% description coverage, yet the description adds no information about their meanings. Parameters like 'recent_feedback' and 'risk_signals' are left entirely unexplained, forcing the agent to guess.
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 action ('Add') and resource ('research insights to Salesforce account records'), making the purpose evident. However, it does not differentiate from siblings like 'add_research_note' or 'create_customer_briefing', which could cause confusion.
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?
There is no guidance on when to use this tool versus alternatives, nor any prerequisites or exclusions. The description simply states what it does without providing context for appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Understand what caused unusual patterns
| Name | Required | Description | Default |
|---|---|---|---|
| anomaly_id | Yes | ||
| context_window | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and description fails to disclose behavioral traits. It does not mention return values, side effects, or how context_window affects output, leaving agent uninformed.
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?
Description is a single sentence, which is concise but lacks necessary detail. It is not verbose, but brevity comes at the cost of informativeness.
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 annotations, no output schema, and two parameters, the description is severely incomplete. It does not tell the agent what output to expect, how to interpret results, or how context_window modifies behavior.
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 0%, and description adds no explanation for parameters. 'anomaly_id' and 'context_window' are self-explanatory from names but description should add context like where to get anomaly_id and what context_window represents (e.g., days).
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?
Description 'Understand what caused unusual patterns' indicates the tool explains anomalies, but it lacks specificity. It does not clearly distinguish from sibling 'detect_anomalies' or specify the nature of the explanation.
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 on when to use this tool vs alternatives (e.g., detect_anomalies). No prerequisites or context provided, such as needing an anomaly_id from detection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Send insights to ProductBoard
| Name | Required | Description | Default |
|---|---|---|---|
| insights | Yes | ||
| board | No | Feature Ideas | |
| auto_tag | No | ||
| include_provenance | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description gives zero behavioral information such as side effects, authentication requirements, rate limits, or data transformation behavior. The agent has no insight into what happens during export.
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 short (one sentence), but this is under-specification rather than conciseness. It fails to earn its place by adding value beyond the tool name.
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?
With four parameters, no output schema, and no descriptions, the tool is highly complex. The single-sentence description is completely inadequate to guide an agent in using the tool correctly.
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 0%, and the description does not mention any of the four parameters (insights, board, auto_tag, include_provenance). The agent cannot infer parameter meanings or usage from the description.
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 'Send insights to ProductBoard' restates the tool name (export_to_productboard) without adding specificity. It does not differentiate from sibling tools like export_to_signal or sync_to_jira, and lacks any scope or context.
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, no prerequisites, and no contextual cues. The agent is left without any decision-making support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Prepare validated insights for Signal platform export
| Name | Required | Description | Default |
|---|---|---|---|
| export_batch_name | Yes | ||
| validation_status_filter | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must fully disclose behavior. It only says 'Prepare validated insights' but does not explain if it exports, creates files, or just queues. Lacks details on effects, permissions, or side effects.
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?
Extremely short and no fluff, but it is under-specified. Could expand slightly without being verbose.
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 only two parameters, no output schema, and no annotations, the description is inadequate. It does not explain the export process, output, or how to handle the tool's result.
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 0%, so description must compensate. It fails entirely to explain the parameters export_batch_name or validation_status_filter, leaving the agent without usage context beyond the schema.
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 mentions 'export' and 'Signal platform', clearly identifying the destination and action. However, the verb 'Prepare' is slightly vague; 'Export' would be more direct. It differentiates from sibling tools like export_to_productboard by specifying Signal.
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 on when to use this tool versus other export tools or validation tools. Missing context like prerequisite that insights must be validated or how export_batch_name is obtained.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Auto-generate executive briefs from multiple calls
| Name | Required | Description | Default |
|---|---|---|---|
| scope | Yes | ||
| output_format | No | executive | |
| include_quotes | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description fails to disclose behavioral traits such as side effects, authorization needs, or whether it is a read or write operation. The description is purely functional.
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 short (one sentence) but under-specified. It is not concise; it lacks necessary details for correct usage.
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 complexity (3 parameters, nested scope object, many sibling tools), the description is entirely insufficient. No output schema is provided, and no context about expected results or integration is given.
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 0% – the description does not explain any of the three parameters (scope, output_format, include_quotes). It does not clarify what the scope object requires or how the enum values affect the output.
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?
Description clearly states the tool generates executive briefs from multiple calls, specifying both output type and input source. However, it does not differentiate from similar siblings like create_customer_briefing or create_insight_snapshot.
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 on when to use this tool versus alternatives (e.g., aggregate_insights_by_theme, create_customer_briefing). No when-not-to-use or preconditions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compare insights across time periods
| Name | Required | Description | Default |
|---|---|---|---|
| comparison_periods | Yes | ||
| scope | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden, yet it does not disclose any behavioral traits such as read-only vs destructive, output expectations, or side effects. 'Compare' is ambiguous.
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 short, but it is under-specified rather than concise. It lacks necessary detail and does not effectively front-load meaning.
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 has no output schema and the description does not mention return values or the complex nested structure of parameters. It is incomplete for a tool with such complexity.
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 0%, meaning no parameter descriptions exist. The tool description fails to explain the parameters 'comparison_periods' and 'scope' or their roles, leaving the agent without additional semantic context.
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 'Compare insights across time periods' clearly states the action (compare) and resource (insights over time). However, it does not differentiate from sibling tools like 'track_cohort_over_time' or 'track_pattern_trends'.
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. There are no pointers to when it is appropriate or 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.
Retrieve contents of a research collection
| Name | Required | Description | Default |
|---|---|---|---|
| collection_id | Yes | ||
| include_provenance | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden for behavioral disclosure. It only states 'retrieve contents' without detailing what 'contents' includes, side effects, or access requirements. The operation is implied as read-only but not explicitly confirmed.
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 brief sentence, which is efficient but lacks structure such as sections or bullet points. It is not overly verbose, but some additional detail could be included without harming 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?
Given the tool has two parameters and no output schema, the description is notably incomplete. It does not mention return format, pagination, or the effect of the 'include_provenance' flag. More context is needed for effective 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 0%, yet the description adds no meaning beyond parameter names. 'include_provenance' is not explained, nor is there any format guidance for 'collection_id'. This leaves the agent without critical context for correct invocation.
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 'Retrieve contents' and the resource 'research collection', making the purpose unambiguous. It also distinguishes from sibling tools like 'get_insight_provenance' which target different data.
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 tools or other retrieval functions. The description lacks context for appropriate usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find competitor mentions across recordings
| Name | Required | Description | Default |
|---|---|---|---|
| competitors | Yes | ||
| limit | No |
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 only states the basic operation without detailing scope, return format, or limitations, leaving significant ambiguity.
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 phrase with no wasted words. However, it is too brief and lacks necessary detail, falling short of being optimally informative.
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 parameters, lack of output schema, no annotations, and many sibling tools, the description is incomplete. It does not specify what the tool returns, the scope of recordings, or any constraints.
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 no descriptions (0% coverage), and the tool description adds no explanation for the parameters 'competitors' or 'limit'. The agent receives no guidance beyond parameter names and types.
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 clear action ('Find') and resource ('competitor mentions across recordings'), making the tool's purpose understandable. It distinguishes from siblings by focusing on competitor mentions, which is unique among the listed sibling tools.
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 many siblings, explicit usage conditions would be beneficial.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Aggregate insights across Sales, Support, and UX workspaces
| Name | Required | Description | Default |
|---|---|---|---|
| workspace_types | No | ||
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral traits. It does not disclose any side effects, limitations, data scope, or permission requirements. The description is too brief to inform an agent about behavioral aspects.
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 unnecessary words. It is concise and front-loaded, but it sacrifices detail for brevity.
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, no annotations, and 2 parameters, the description is incomplete. It does not specify what kind of insights are returned, how results are structured, or any usage context relative to siblings.
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 0%, but the description mentions the three workspace types which correspond to the enum in workspace_types, adding some meaning. However, it does not explain the 'limit' parameter or any constraints on workspace_types.
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 it aggregates insights across specific workspaces (Sales, Support, UX), which gives a clear verb-resource pair. However, 'insights' is vague and doesn't distinguish from similar aggregation tools like aggregate_insights_by_theme.
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 on when to use this tool versus alternatives like aggregate_insights_by_theme or generate_trend_analysis. There are many sibling tools, and the description fails to explain what differentiates this cross-workspace aggregation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get full citation with timestamps and evidence
| Name | Required | Description | Default |
|---|---|---|---|
| insight_id | Yes |
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 only states that the tool returns a citation with timestamps and evidence, but does not disclose whether it is read-only, any prerequisites (e.g., insight must exist), potential side effects, or response format. This is insufficient for a safe and correct invocation.
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, which is concise and front-loaded. However, it lacks structure and detail; it does not earn its place by providing sufficient information. While brevity is good, it sacrifices clarity and completeness.
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 simplicity (one parameter, no output schema, no annotations), the description is incomplete. It does not explain what constitutes a 'citation' or 'evidence', nor does it describe the return format or behavior. This leaves the agent without crucial context for proper 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 0%, meaning the input schema provides no descriptions for the parameter 'insight_id'. The tool description does not mention this parameter at all, failing to add meaning beyond the schema. For a single required parameter, this is a critical omission.
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 'Get full citation with timestamps and evidence' clearly states the verb 'Get' and the resource 'citation' (provenance). It specifies what the tool returns (timestamps and evidence), which is specific and distinguishes it from sibling tools like 'create_insight_snapshot' or 'explain_anomaly'. However, it does not explicitly connect to the insight context implied by the name, leaving a small gap.
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. It does not mention any context, prerequisites, or exclusions. With many sibling tools related to insights (e.g., 'get_cross_workspace_insights', 'search_research_history'), the lack of usage direction is a significant gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
See what team members have noted
| Name | Required | Description | Default |
|---|---|---|---|
| scope | No | ||
| include_unresolved_questions | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It implies a read operation but fails to disclose idempotency, side effects, auth requirements, or return value details, which is insufficient for a 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 extremely concise (one short sentence), which is efficient but sacrifices necessary detail. It is front-loaded but lacks substance.
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, complex nested parameters, and a large sibling set, the description is incomplete. It fails to specify what the tool returns or how parameters affect behavior, leaving significant gaps for effective 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 0%, and the tool's text description does not mention or explain any of the two parameters ('scope' and 'include_unresolved_questions'). It adds no meaning beyond the schema, leaving parameter semantics entirely opaque.
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 'See what team members have noted' conveys a read operation on team annotations, but it's vague and doesn't specify what type of annotations or the output format. It lacks differentiation from sibling tools like add_research_note or get_validation_queue.
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 on when to use this tool versus alternatives is provided. The description doesn't mention context, prerequisites, or exclusions, leaving the agent without decision support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get insights pending manual review
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| min_confidence | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It states 'Get' implying a read operation, but does not explicitly confirm safety, idempotency, or any side effects. The minimal description offers no additional 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 very concise (4 words). While front-loaded, it is too brief to be adequately informative. It could include more details without sacrificing conciseness, so it does not fully earn its place.
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 simplicity (2 params, no output schema), the description should explain what is returned (e.g., structure of insights) and default behavior. It only says 'insights pending manual review', leaving the agent without sufficient context about the tool's output or scope.
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 2 parameters (limit, min_confidence) with 0% description coverage in the schema. The description does not mention or explain these parameters, failing to add value beyond the schema. For a low-coverage case, the description should compensate but does not.
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 resource ('insights pending manual review'), effectively distinguishing it from siblings like 'search_by_validation_status' which implies searching rather than fetching. However, it could be more specific about what 'validation queue' entails.
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_by_validation_status' or 'validate_insight_batch'. The description lacks any context about prerequisites or scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
What causes positive/negative reactions
| Name | Required | Description | Default |
|---|---|---|---|
| sentiment | Yes | ||
| min_intensity | No | ||
| context_window | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses no behavioral traits. With no annotations provided, the description carries full burden but fails to mention any side effects, permissions, or operational details (e.g., what triggers are identified, how intensity is used).
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 short (one phrase), but this is under-specification rather than conciseness. It lacks essential details and is not front-loaded with actionable 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?
Given the tool has 3 parameters, no annotations, and no output schema, the description is severely incomplete. It provides almost no context for correct invocation or interpretation of results.
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 0%, meaning the description must explain parameter meanings. It does not mention sentiment, min_intensity, or context_window at all, leaving the agent to infer from parameter names alone.
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 is a vague question ('What causes positive/negative reactions') rather than a clear statement of the tool's action. It does not specify a verb or resource, and fails to distinguish from sibling tools like analyze_sentiment_shifts.
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 vs alternatives. There is no mention of context, prerequisites, or exclusions, leaving the agent without decision support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find stages with missing feedback/issues
| Name | Required | Description | Default |
|---|---|---|---|
| journey_template | No | saas_b2b | |
| date_range | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It only states 'Find...', implying a read operation, but does not confirm read-only nature, data effects, or authorization needs. This is insufficient for an AI agent to assess side effects.
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 5-word sentence, which is concise but lacks substance. While brevity is good, it does not earn its place by covering essential details, making it minimally acceptable.
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 has 2 parameters (one nested object), no output schema, and no annotations, the description is severely incomplete. It omits what 'stages' refers to, how templates affect results, and what the output looks like, leaving the AI agent with insufficient context.
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 0% description coverage for its properties, and the tool description fails to explain the 'journey_template' enum values or the structure of 'date_range'. The description adds no meaning beyond the schema, missing critical parameter semantics.
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 'Find stages with missing feedback/issues' clearly indicates the tool identifies gaps in feedback across journey stages. However, it does not differentiate from the sibling tool 'identify_knowledge_gaps', which could be seen as overlapping.
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 like 'map_customer_journey' or 'identify_knowledge_gaps'. The description lacks any context about prerequisites or appropriate use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find what you don't know
| Name | Required | Description | Default |
|---|---|---|---|
| topic | Yes | ||
| compared_to | No | industry_benchmarks |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, and the description provides no behavioral traits such as read-only, destructive, or authorization needs. Full burden is on description, which offers nothing.
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 short, but it's under-specified rather than concise. Every sentence should earn its place, but this one doesn't.
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, no annotations, and a large set of sibling tools, the description is utterly incomplete for an agent to use effectively.
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 0%; the description does not explain the 'topic' parameter or the 'compared_to' enum options, leaving the agent without needed context.
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 'Find what you don't know' is vague and does not specify a clear verb+resource. It fails to distinguish from sibling tools like 'identify_emotional_triggers' or 'identify_journey_gaps'.
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 on when to use this tool vs alternatives, no exclusions or context provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Load saved search filters
| Name | Required | Description | Default |
|---|---|---|---|
| filter_id | No | ||
| filter_name | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It only states 'Load saved search filters', implying retrieval but not specifying whether it is read-only, how it handles multiple parameters, error states, or return format. Critical behavioral context is missing.
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 only four words, which is too terse. It lacks structure or front-loading of key information. While brevity is valued, it sacrifices clarity and fails to provide necessary details.
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 has two optional parameters and no output schema, the description is severely incomplete. It does not explain return values, behavior when both parameters are given, or any constraints. The agent cannot infer correct usage from this alone.
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 0%, and the description provides no explanation of the two optional parameters (filter_id and filter_name). The agent has no guidance on what these parameters represent, their relationship, or typical usage. This is a complete lack of semantic information.
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 'Load saved search filters' uses a clear verb 'Load' and specifies the resource as 'saved search filters', which differentiates it from sibling tools like 'save_search_filter'. However, it could be more precise about what 'load' entails (e.g., retrieve, display, apply) and lacks detail to fully distinguish from similar retrieval tools.
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 usage guidelines are provided. The description does not indicate when to use this tool versus alternatives like 'search_insights_by_scope', nor does it explain conditions for using filter_id vs filter_name. This leaves the agent without context for proper selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Link insights to customer journey stages
| Name | Required | Description | Default |
|---|---|---|---|
| customer_id | Yes | ||
| journey_stages | No | ||
| include_timeline | No |
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 only states the action without explaining side effects, return values, or whether the tool creates, updates, or reads data. This is insufficient for a tool with no embedded safety hints.
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?
While the description is a single sentence, it is under-specified and lacks structure. It does not front-load key information or earn its place with value; rather, it is incomplete.
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 has 3 parameters, no output schema, and no annotations, the description is severely incomplete. It fails to explain what the tool outputs, prerequisites, or relationships to the 44 sibling tools, leaving the agent with insufficient context.
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 0%, meaning no parameter descriptions exist in the schema. The description adds no explanations for the three parameters (customer_id, journey_stages, include_timeline), so the agent must guess their meaning only from names.
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 'Link insights to customer journey stages' indicates a verb ('link') and a resource ('insights to customer journey stages'), but 'insights' is vague and the scope is unclear. It does not differentiate from siblings like 'identify_journey_gaps' or 'track_cohort_over_time'.
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. No when-not-to-use conditions or context are given, leaving the agent to infer usage entirely.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Alert when research metrics hit thresholds
| Name | Required | Description | Default |
|---|---|---|---|
| kpi | Yes | ||
| feature | No | ||
| threshold | Yes | ||
| action | No | notify |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. The description implies a monitoring/alerting behavior but doesn't disclose whether it persists alerts, sends notifications, or requires setup. The 'action' parameter default 'notify' is not explained.
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 short sentence, which is concise, but it lacks structure and fails to provide necessary details. It is not verbose but incomplete.
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?
With 4 parameters, 0% schema documentation, and no output schema, the description is insufficient. It doesn't cover how thresholds are configured, what the alert looks like, or any return value.
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 for parameters is 0%. The description offers no explanation for 'kpi', 'feature', 'threshold' (object with unknown structure), or 'action' options. Users cannot infer valid inputs.
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 it alerts on threshold hits for research metrics, but 'research metrics' is vague and doesn't distinguish from sibling 'create_research_alert'. It lacks specificity about what constitutes a KPI or metric.
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 on when to use this tool vs alternatives like 'create_research_alert', or prerequisites. The description simply states the function without context for decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Manual override of validation status
| Name | Required | Description | Default |
|---|---|---|---|
| insight_id | Yes | ||
| new_validation_status | Yes | ||
| override_reason | Yes | ||
| overridden_by | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description must disclose behavioral traits. It only says 'override', but does not clarify if it bypasses normal rules, logs changes, or affects other records. No mention of permissions or reversibility.
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, which is concise, but it lacks structure. It is not front-loaded with key details and misses important 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 tool with 4 parameters (3 required), no output schema, and no annotations, this description is severely incomplete. It fails to explain what the tool does beyond the literal name, leaving agents without critical context.
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 0%; the description adds no explanation for any of the 4 parameters. Even the required 'override_reason' lacks guidance on expected content, and the optional 'overridden_by' is unexplained.
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 overrides validation status, distinguishing it from siblings like validate_insight_batch (batch processing) and search_by_validation_status (search). The verb 'override' is specific, though the resource 'validation status' is implied.
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 on when to use this tool versus alternatives like validate_insight_batch or when not to use it. It does not state prerequisites, exceptions, or comparison with other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
ML-based prediction of validation outcome
| Name | Required | Description | Default |
|---|---|---|---|
| insight_id | Yes |
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 'ML-based prediction' implying a read operation, but does not explicitly confirm it is non-destructive, nor discloses any rate limits, access requirements, or side effects. This lack of detail reduces 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 very short but under-specified. It uses one vague sentence that fails to convey essential information. Conciseness should not sacrifice clarity; here it does.
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 and one parameter, the description should explain what the tool returns, any constraints, and how it integrates with other tools. It only provides a high-level label, leaving significant gaps in completeness.
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 single parameter 'insight_id' has no description in the schema (0% coverage). The tool description does not explain what an insight_id is, its format, or acceptable values. The description adds no semantic value beyond the schema itself.
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 specifies the verb 'prediction' and the resource 'validation outcome', distinguishing it from sibling tools like 'validate_insight_batch' which perform actual validation. However, it does not clarify what exact outcome is predicted (e.g., likely pass/fail, confidence 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?
No guidance is provided on when to use this tool versus alternatives such as 'validate_insight_batch' or 'override_validation'. The absence of context makes it difficult for an agent to decide between related tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Save complex filter combinations for quick recall
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | ||
| filters | Yes | ||
| description | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description should disclose behavioral traits. It only mentions 'for quick recall', implying persistence, but fails to specify behavior on duplicate names, overwrite rules, or authorization 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?
While the description is a single sentence, it is too brief for a tool with 3 parameters and a nested object. It sacrifices completeness for brevity, lacking front-loaded key 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?
Given the tool has 3 parameters (with required nested object), no output schema, no annotations, and many siblings, the description is severely inadequate. It fails to explain the filter object structure, save behavior, or relationship to load_search_filter.
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 coverage is 0%, and the description adds no information about the parameters beyond what is already in the property names. The nested 'filters' object is completely unexplained.
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 verb ('Save') and resource ('complex filter combinations'), and the sibling tool 'load_search_filter' provides natural distinction between save and load 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?
No guidance is given on when to use this tool versus alternatives like 'load_search_filter' or other filter-related tools. The description lacks context about prerequisites or typical use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Filter insights by confidence score range
| Name | Required | Description | Default |
|---|---|---|---|
| min_confidence | No | ||
| max_confidence | No | ||
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It only states it filters insights, but does not indicate if this is a read-only operation, whether it requires authentication, or any side effects. The agent lacks information about data safety or permissions.
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, making it concise. However, it is under-specified; while short, it does not fully convey the tool's capabilities or constraints. It is acceptable for a simple filter but lacks depth.
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 and no annotations, the description should provide more context about return format, pagination behavior (e.g., how limit affects results), and edge cases. The tool has 3 parameters but the description does not mention how they interact. This is incomplete for an agent to invoke correctly.
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 0%, meaning no parameter descriptions in the input schema. The description does not add any explanation about the parameters beyond their names and schema types. For example, the 'limit' parameter's purpose is not explained. The description adds minimal value over the schema.
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?
Description clearly states the tool filters insights by confidence score range. It uses a specific verb (filter) and resource (insights) with scope (confidence score range). Among sibling tools like search_by_validation_status and search_insights_by_scope, this description differentiates it based on confidence filtering, but does not explicitly mention why one would choose this over others.
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 explicit guidance on when to use this tool versus alternatives. The description does not provide context about prerequisites, when not to use, or mention sibling tools. For a filtering tool, it would be helpful to know typical use cases or if it should be combined with other searches.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Filter insights by validation status
| Name | Required | Description | Default |
|---|---|---|---|
| validation_status | Yes | ||
| limit | No | ||
| offset | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It fails to mention pagination behavior (limit, offset default and limits), order of results, or whether the tool is read-only. A description like 'Returns insights (possibly paginated) that match the given validation statuses' would be minimal but this is absent.
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 short (6 words) but at the cost of missing critical details. Conciseness should not sacrifice completeness. A single sentence can be concise and informative, but here it omits essential usage info.
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 lack of output schema, low annotation coverage, and three parameters, the description is too sparse. It does not explain the expected response, pagination, or how results are ordered. For a filter tool, returning filtered list is implied but details about offset, limit behavior, and possible empty results are needed.
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 0% and the description does not explain any parameter. The validation_status parameter is required and has an enum list, but the description doesn't clarify its role or format. The limit and offset parameters are not mentioned at all. The description adds no value over the raw schema.
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 'Filter insights by validation status' clearly states the verb (filter) and resource (insights) with the specific criterion (validation status). This distinguishes it from siblings like search_by_confidence (different field) and get_validation_queue (which implies a list of items needing action). However, it could be more specific about what 'insights' are and whether it returns a list or count.
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?
There is no guidance on when to use this tool over alternatives. Sibling tools like get_validation_queue, override_validation, and predict_validation_outcome serve different purposes but no comparison or exclusion criteria are provided. The description does not mention context or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search insights with scoped filters (call_type, sentiment, date_range, product, segment, quality_threshold). Handles bulk queries of 1500+ calls.
| Name | Required | Description | Default |
|---|---|---|---|
| scope | Yes | ||
| limit | No | ||
| offset | No |
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 'Handles bulk queries' which suggests performance capability, but it does not disclose any behavioral traits like side effects, authorization needs, or rate limits. For a search tool, read-only nature is implied but not explicitly stated.
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 sentences with no wasted words. The first sentence states the purpose and lists filters; the second adds a capability. It is front-loaded and clear, though slightly under-specified.
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 has a nested scope object and no output schema. The description does not mention pagination (limit/offset), required fields, or response format. The 'bulk queries' hint is vague. Given the complexity of the scope object, more detail is needed.
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 description lists filters like 'product' and 'segment' that are not in the input schema, causing potential confusion. Schema has some descriptions for 'call_type' and 'sentiment', but the description adds nothing beyond field names. Since schema description coverage is 0% (or very low), the description fails to compensate.
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 insights with scoped filters, listing filter types. It specifies the action and resource, and the inclusion of 'bulk queries' hints at scale. However, it does not explicitly distinguish from sibling tools like search_by_confidence or search_recordings_metadata.
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 on when to use this tool versus alternatives. The only hint is 'bulk queries of 1500+ calls', implying it is for large datasets, but no explicit when-to-use or when-not-to-use information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search recordings by date range
| Name | Required | Description | Default |
|---|---|---|---|
| date_range | No | ||
| limit | No | ||
| offset | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description should disclose behavioral traits. It implies a read operation ('search'), but does not explicitly state it is read-only, nor does it mention rate limits, authentication, or side effects. The tool name suggests metadata is returned, but the description says 'search recordings', which is slightly ambiguous.
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, which is concise. However, it lacks structure such as bullet points or separate sections. For a tool with three parameters, it is acceptably brief but could include more detail without being overly verbose.
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 has three parameters (including a nested object), no output schema, and no annotations, the description is incomplete. It does not explain pagination, result format, or whether it returns metadata or full recordings. The context of sibling search tools suggests more specificity is needed.
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 0%, meaning the schema lacks property descriptions. The description mentions date range, which correlates with the date_range parameter, but does not explain limit and offset for pagination. It adds minimal meaning beyond the raw schema.
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 'search' and the resource 'recordings' with a specific filter 'by date range'. However, it does not distinguish this tool from sibling search tools like search_by_confidence or search_by_validation_status, so it lacks full differentiation.
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. Given the many sibling search tools, the description should indicate that this tool is for date-range-based searches and that other tools are for other criteria. There is no such guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find similar past research
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| timeframe | No | last_12_months | |
| similarity_threshold | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits but only states the purpose. It does not mention whether the tool is read-only, uses embeddings, or requires specific permissions, offering minimal 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 extremely short (four words), which is under-specification rather than conciseness. It lacks structure and fails to provide essential information, making it minimally useful.
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 three parameters, no output schema, and no annotations, the description is severely incomplete. The agent cannot infer return format, parameter constraints, or behavioral nuances, rendering the tool poorly defined for reliable invocation.
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 0%, yet the description adds no meaning to the three parameters (query, timeframe, similarity_threshold). The agent has no understanding of how these parameters affect behavior, making the description completely inadequate for parameter semantics.
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 'Find similar past research' is vague. It states the action (find) and subject (research) but does not clarify what 'similar' means or differentiate from sibling tools like search_by_confidence or search_insights_by_scope.
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. The description does not specify the kind of similarity (e.g., semantic or keyword) or indicate any conditions for use, leaving the agent without context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
AI suggests follow-up questions based on data gaps
| Name | Required | Description | Default |
|---|---|---|---|
| current_findings | Yes | ||
| research_goal | Yes | ||
| target_audience | No | enterprise |
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 mentions AI involvement but does not disclose whether the operation is read-only, any side effects, or permission 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 a single short sentence, which is concise. However, it could be expanded to include more detail without losing 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?
With three parameters and no output schema, the description is incomplete. It does not explain input format, how questions are generated, or what the return value looks like.
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 description adds no meaning to any of the three parameters. Schema description coverage is 0%, and the description does not explain what current_findings, research_goal, or target_audience represent.
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 suggests follow-up questions based on data gaps, which is a specific verb and resource. This distinguishes it from sibling tools that identify gaps or generate briefs.
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 usage guidance is provided. The description does not indicate when to use this tool instead of alternatives like identify_knowledge_gaps or generate_research_brief.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create Jira tickets from high-frequency feature requests
| Name | Required | Description | Default |
|---|---|---|---|
| feature_requests | Yes | ||
| project_key | No | PROD | |
| issue_type | No | Feature Request | |
| auto_populate | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must carry behavioral disclosure. It implies mutation (creating tickets) but does not state idempotency, duplication behavior, authentication requirements, or side effects. The agent cannot assess safety or 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 single sentence is concise and front-loaded with the core action, but it omits necessary details at the expense of brevity. Conciseness alone does not compensate for insufficient 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?
Given 4 parameters (including a nested object) and no output schema, the description is severely incomplete. It fails to cover parameter behavior, return values, expected outcomes, or edge cases, leaving the agent with critical 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 0%, yet the description adds no parameter explanations. 'feature_requests' and 'auto_populate' (a nested object) are left entirely ambiguous. The agent lacks the semantic understanding needed to fill parameters correctly.
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 (create Jira tickets) and the source (high-frequency feature requests). It distinguishes from sibling tools which focus on analysis or research, as this tool is an export/sync action to an external system.
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 guidelines on when to use this tool versus alternatives. The description does not mention prerequisites, when to avoid using it, or specify any conditions that trigger its use. The agent has no context for appropriate invocation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Validate research hypotheses with data
| Name | Required | Description | Default |
|---|---|---|---|
| hypothesis | Yes | ||
| null_hypothesis | Yes | ||
| confidence_level | No | ||
| sample_size_min | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are absent, and the description provides no behavioral details beyond the verb 'validate'. It does not disclose whether the tool is read-only, destructive, or requires permissions, nor does it explain the statistical methodology or side effects.
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 a single sentence, but it lacks structure (e.g., bullet points or sections) and omits critical details. It is efficient but incomplete.
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 absence of an output schema, zero annotation coverage, and four unparameterized inputs, the description fails to convey essential information about return values, constraints, or expected behavior, leaving the agent underinformed.
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?
With 0% schema description coverage and no explanation in the description, the meaning of parameters like 'hypothesis', 'null_hypothesis', 'confidence_level', and 'sample_size_min' is entirely opaque. The agent cannot infer appropriate values or constraints.
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 validates research hypotheses with data, indicating a statistical hypothesis testing function. However, it does not distinguish it from sibling tools like 'validate_insight_batch' or 'calculate_sample_size', lacking specificity about the method (e.g., t-test, chi-square).
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. There is no mention of prerequisites, context, or when not to use it, leaving the agent without decision support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
See how a cohort's feedback evolves
| Name | Required | Description | Default |
|---|---|---|---|
| cohort | Yes | ||
| time_buckets | No | monthly | |
| start_date | Yes | ||
| end_date | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description does not disclose any behavioral traits (e.g., read-only, output format, side effects). The description fails to inform the agent about important safety or usage constraints.
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?
Extremely short (one sentence) but lacks essential details. It is front-loaded but too terse to be useful. Every sentence does not earn its place because it omits critical 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?
Given 4 parameters (including a nested object), no output schema, and no annotations, the description is severely incomplete. It fails to explain what 'evolution' means, how to construct the cohort, or what data is returned.
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?
0% schema description coverage; the description adds no meaning to any parameter. The 'cohort' object is vague, and dates are unexplained. The description does not clarify how parameters relate to the tool's function.
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?
Description says 'See how a cohort's feedback evolves', which indicates monitoring feedback over time but uses vague verb 'see'. Distinguishes from sibling 'compare_cohorts' (comparison) and 'track_pattern_trends' (trends across patterns), but is less specific than it could be.
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 on when to use this tool versus alternatives. No context for prerequisites or exclusions. Only implies evolution over time.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compare patterns across time periods
| Name | Required | Description | Default |
|---|---|---|---|
| pattern | Yes | ||
| comparison_periods | No | ||
| pattern_type | No | all |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description does not disclose behavioral traits such as whether the tool performs read-only analysis or has side effects, and no annotations are provided to compensate.
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 very concise at only 4 words, but it sacrifices informativeness; it is not verbose but fails to convey key details.
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 has 3 parameters with no schema descriptions and no output schema, the description is insufficient to enable correct usage, missing return value information and parameter guidance.
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 0% description coverage, and the tool description does not explain any parameters (pattern, comparison_periods, pattern_type), leaving the agent without needed context.
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 'Compare patterns across time periods' clearly states the tool's action and resource, but it lacks differentiation from siblings like 'detect_recurring_patterns' or 'generate_trend_analysis', making it less distinctive.
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, nor are there any conditions or exclusions mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Record usage events from Signal platform
| Name | Required | Description | Default |
|---|---|---|---|
| export_batch_id | Yes | ||
| usage_event | Yes | ||
| usage_details | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits like whether it's a write operation, if it requires specific permissions, or any side effects. The agent is left guessing.
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 at 8 words, but it lacks necessary detail. While not verbose, it sacrifices informativeness for brevity.
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 (3 parameters, nested object, no output schema, no annotations), the description is severely incomplete. It fails to explain return behavior, usage context, or parameter 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?
With 0% schema description coverage and no annotation, the description 'Record usage events from Signal platform' adds no meaning to the three parameters (export_batch_id, usage_event, usage_details). The agent cannot infer their roles.
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 'Record usage events from Signal platform' clearly states the action and resource, but does not differentiate from sibling tools like audit_data_usage or track_cohort_over_trends, which have similar purposes.
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 on when to use this tool versus alternatives, such as export_to_signal for exporting data or audit_data_usage for auditing. Lacks context on prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Bulk validate multiple insights
| Name | Required | Description | Default |
|---|---|---|---|
| insight_ids | Yes | ||
| validation_status | Yes | ||
| reason_codes | No | ||
| reviewer_notes | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description should reveal behavioral traits. It only says 'Bulk validate', implying mutation of multiple insights, but lacks details on side effects (e.g., irreversible changes), required permissions, or how validation updates are persisted. The description adds minimal value beyond the tool name.
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 phrase with no structure or sentences. While short, it is under-specified to the point of being uninformative. Conciseness is not achieved at the expense of clarity; here it sacrifices 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?
Given the tool's 4 parameters, no output schema, and many sibling tools, the description is critically incomplete. It does not explain the return value, validation behavior, or how results are communicated. The agent lacks sufficient context to use the tool correctly.
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 0%, so the description must compensate. However, it provides no explanation of the four parameters, including the enum for validation_status, the role of reason_codes, or how reviewer_notes are used. The description adds zero semantic value for parameter 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 'Bulk validate multiple insights' clearly specifies a verb ('validate') and a resource ('insights') with a batch scope. However, it does not differentiate from sibling tools like 'override_validation' or 'get_validation_queue', and could be more precise about what 'validate' entails (e.g., changing status).
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 usage guidance is provided. The description does not mention when to use this tool versus alternatives such as 'override_validation' or 'search_by_validation_status', nor does it specify prerequisites or typical scenarios.
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.
v1.0.0add_research_noteaggregate_insights_by_themeanalyze_feature_requestsanalyze_sentiment_shiftsanonymize_insightsassess_research_qualityaudit_data_usageauto_tag_recordingsbatch_apply_tagscalculate_confidence_distributioncalculate_sample_sizecompare_cohortscreate_customer_briefingcreate_insight_snapshotcreate_research_alertcreate_stakeholder_reportdetect_anomaliesdetect_recurring_patternsdetect_research_biasenrich_salesforce_accountexplain_anomalyexport_to_productboardexport_to_signalgenerate_research_briefgenerate_trend_analysisget_collection_itemsget_competitor_mentionsget_cross_workspace_insightsget_insight_provenanceget_team_annotationsget_validation_queueidentify_emotional_triggersidentify_journey_gapsidentify_knowledge_gapsload_search_filtermap_customer_journeymonitor_kpi_thresholdsoverride_validationpredict_validation_outcomesave_search_filtersearch_by_confidencesearch_by_validation_statussearch_insights_by_scopesearch_recordings_metadatasearch_research_historysuggest_research_questionssync_to_jiratest_hypothesistrack_cohort_over_timetrack_pattern_trendstrack_signal_usagevalidate_insight_batchScored across 52 tools
Many tools share similar verbs like 'search', 'detect', and 'analyze', making it difficult for an agent to distinguish between them without careful reading of descriptions. For instance, there are five search-related tools and three detect tools, creating potential confusion.
All tool names follow a consistent verb_noun pattern using underscores, e.g., 'add_research_note', 'analyze_feature_requests', 'generate_trend_analysis'. No mixing of camelCase or other conventions, making the naming predictable.
With 52 tools, the server is significantly over-scoped for a typical MCP server. The number feels excessive for coherent use, and many tools could be split into smaller, more focused servers.
The tool surface covers a wide range of research insight operations, but lacks basic CRUD functionality for notes and tags (e.g., no delete or update for notes, no remove tags). Additionally, there is no tool for importing new recordings, which is a notable gap.
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