equities_conceptFull observation history for one XBRL concept (any us-gaap tag). Use for fields outside our 35-item headline catalog. Returns every observation across every reported unit, most-recent first.
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | Yes | ||
| concept | Yes | us-gaap XBRL tag, e.g. ResearchAndDevelopmentExpense. | |
| include_segments | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With readOnlyHint, idempotentHint, and non-destructive annotations already present, the description leverages that coverage and adds behavioral context: 'Returns every observation across every reported unit, most-recent first.' This tells the agent about completeness and ordering without needing to re-state safety. No contradiction with 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?
Two sentences front-load the core behavior, then usage, then ordering, with no waste or repetition. Every sentence adds value and the structure is easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers purpose, usage, and ordering, but because there is no output schema it does not describe what an observation record contains (e.g., date, value, filer). The unexplained include_segments parameter is another gap, leaving an otherwise simple tool slightly incomplete for an 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?
Only concept has schema documentation, and the description merely echoes 'any us-gaap tag' without adding format details. ticker is left as a bare string with no accepted format, and include_segments has no explanation in either the schema or the description. Given 33% schema description coverage, the description fails to compensate for two of the three parameters.
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 'Full observation history for one XBRL concept (any us-gaap tag)', naming the specific verb, resource, and scope. It also explicitly differentiates the tool from the '35-item headline catalog', clarifying its niche among siblings. The title reinforces the same content.
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 sentence 'Use for fields outside our 35-item headline catalog' explicitly states when to choose this tool and implicitly excludes catalog fields. However, it does not name the alternative tool (e.g., equities_fundamentals) that provides the catalog, so the alternative remains implied rather than explicit.
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.
Multiple tools have genuinely blurry boundaries: company_change vs company_changes differ only by singular/plural yet serve different purposes, company_domain vs company_classify vs company_lookup_auto all accept a domain, geo_zip_lookup vs geo_enrich vs geo_zip_batch all return ZIP profiles, and email_validate subsumes much of email_disposable and email_free_provider. The domain prefixes help narrow search space, but within many domains an agent cannot reliably predict which tool is the right one.
All 129 tools uniformly follow a snake_case [domain]_[topic] convention (company_, fx_, geo_, dns_, weather_, tax_), which is highly predictable and consistent. Minor deviations include the confusing company_change/company_changes pair, and inconsistent suffix usage (_batch appears on address_validate_batch, company_domains_batch, geo_zip_batch but not on equivalent lookup tools elsewhere).
129 tools far exceeds the 50+ extreem-mismatch threshold, bundling roughly 28 unrelated data domains (weather, fx, tax, ccompany, dns, jobs, flight, email, phone, tax...) into a single MCP surface. Even focusing on one domain forces the agent to load an enormous unrelated tool list; this should be split into many smaller domain-specific servers.
Per-domain coverage is impressively thorough: weather spans current/forecast/hourly/historical/normals/marine/route/air-quality, fx covers rates/convert/historical/volatility/correlation/strenth, and company includes lookup/enrichment/networks/timeline/peer-comparison plus six buyer-tuned signals with profile-introspection tools. Minor gaps like flight being historical-only and smtp probes skipping major email providers are documented scope decisions rather than dead ends.