Traverse a person's graph one edge per call (relation=): career positions, board seats, education, affiliated deals/funds, advisory roles. Call this when the user asks where a founder or exec worked before, what boards they sit on, or which deals they touched.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| relation | No | ||
| person_id | Yes | Path parameter 'person_id' (required). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavior. It does disclose a key behavior: 'one edge per call (relation=)' which explains how the tool operates and that each call returns a single relation type. However, it does not describe the return format, whether direction matters, or how person_id is obtained, leaving gaps in behavioral 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 exceptionally concise: two sentences. The first states the core functionality and lists relation types; the second gives practical usage examples. Every sentence earns its place, with no wasted words or redundant detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with 3 parameters and no output schema, the description covers the main functionality and usage scenarios well. It does not mention return value structure or how to obtain person_id, but given the low complexity and supportive schema (enum values, defaults), the description is largely sufficient for an agent to select and invoke 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 coverage is only 33% (only person_id has a description). The description enumerates the relation types which adds some meaning beyond the raw enum, but it does not explain the limit parameter or provide guidance on how to use person_id beyond what the schema says. The description adds limited value for parameters, so it partially compensates for the coverage gap but not fully.
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 traverses a person's graph one edge per call, naming specific relation types (career positions, board seats, education, affiliated deals/funds, advisory roles). It distinguishes from sibling tools by focusing on a person's relations and explicitly lists the relation categories, leaving no ambiguity about what the tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool: 'Call this when the user asks where a founder or exec worked before, what boards they sit on, or which deals they touched.' This provides clear usage context. However, it does not explicitly mention alternatives or exclusions (e.g., use other *_relations tools for companies or deals), so it stops short of full comparative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
With 336 tools, there is substantial overlap. Over a dozen health/status tools share nearly identical 'is the system healthy?' descriptions (e.g., tengu_status, tengu_ready, tengu_ml_health, tengu_v3_system_health, tengu_v3_stream_status), and multiple single-ticker analysis (tengu_ml_predict, tengu_copilot_score_ticker, tengu_v3_intel_ml_prediction) and top-picks (tengu_copilot_top_picks, tengu_ml_top_picks, tengu_v3_trade_setups) tools have poorly defined boundaries. Agents would frequently misselect.
The server mixes no-version (tengu_crypto), v2 (tengu_v2_drift), v3 (tengu_v3_intel_*), and copilot (tengu_copilot_*) families, and within families there is inconsistent verb/noun ordering (tengu_v3_research_fetch_url vs tengu_v3_news_summary). While subfamilies like tengu_v3_private_markets_* are internally consistent, the overall naming pattern is chaotic and unpredictable.
336 tools is far beyond any reasonable tool set size, even for an all-in-one financial data platform. This extreme count creates choice paralysis, high latency in tool selection, and makes the server effectively unusable for autonomous agents. The calibration guideline marks 50+ as extreme; this is nearly 7x that threshold.
The platform covers a vast domain: equity and crypto prices, fundamentals, insider trading, options, news (including crypto and FX), private markets, streaming data, risk metrics, and execution planning. There are minor gaps (no direct multi-ticker comparison tool, no order placement), but the surface is remarkably comprehensive for an analysis-focused server.
