Advisory-firm profile by sp_id — the law firm, investment bank, auditor or consultancy behind private-market deals: type, employees, parent, and count of companies serviced. Call this when the user asks who advised a deal or how big an adviser is; use service_provider_relations for its client list.
| Name | Required | Description | Default |
|---|---|---|---|
| sp_id | Yes | Path parameter 'sp_id' (required). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the transparency burden. It adds context by describing the entity type and profile fields, but it does not explicitly state that the operation is read-only, nor does it disclose error states, data freshness, or response format. Some context is provided but not full behavioral disclosure.
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 dense sentences with the core purpose front-loaded, followed by concrete details and usage guidance. No wasted words; every clause earns 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?
For a single-parameter read-only profile lookup with no output schema, the description covers the return fields, when to use it, and a related tool. It does not explain how to discover sp_id, but that is reasonably left to sibling search tools, so the description is adequate if not exhaustive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but the schema description is generic ('Path parameter sp_id'). The tool description adds meaningful semantics by explaining that sp_id identifies an advisory firm and by listing the output fields, making the parameter's purpose clear.
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?
Clear and specific: 'Advisory-firm profile by sp_id' identifies the resource and operation, enumerates the returned fields (type, employees, parent, count of companies serviced), and distinguishes the tool from the sibling service_provider_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?
Explicitly states when to call: 'Call this when the user asks who advised a deal or how big an adviser is' and names the alternative for a related query: 'use service_provider_relations for its client list.'
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.
