Traverse a deal's graph one edge per call (relation=): investors, tranches, debt lenders, sellers, service providers, bonds, loans, distribution beneficiaries. Call this when the user asks who funded, lent into, or sold in a specific round after fetching the deal itself.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| deal_id | Yes | Path parameter 'deal_id' (required). | |
| relation | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral disclosure burden. It does disclose 'one edge per call' and the dependency on fetching the deal first, but it does not mention pagination/limit behavior, return format, or whether the 'relation' parameter is effectively required despite schema only requiring deal_id.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loads the core action, enumerates the relation types compactly, and ends with a practical usage note. No unnecessary words or repetition.
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 annotations and output schema, the description is reasonably complete for tool selection and invocation, but gaps remain: it does not explain the return shape, how 'limit' interacts with traversal, or whether 'relation' can be omitted. These are important for a traversal-style tool and prevent a higher score.
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 only 33%, so the description must compensate. It does meaningfully clarify the 'relation' parameter by listing valid edge types and mapping them to user intents ('who funded, lent into, or sold'), adding value beyond the bare enum. The 'limit' and 'deal_id' parameters still rely mostly on schema defaults and names, but the key ambiguous parameter is well handled.
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 opens with a specific verb and resource: 'Traverse a deal's graph one edge per call (relation=)' followed by the exact edge types. This clearly distinguishes it from deal-fetching tools and other relation-based sibling tools by focusing on 'deal' relations.
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?
It gives an explicit trigger: 'Call this when the user asks who funded, lent into, or sold in a specific round after fetching the deal itself.' This provides clear usage context and sequencing, though it does not explicitly name alternatives or exclusion cases.
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.
