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factanker

by com.factanker
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get_facts

Read-onlyIdempotent

PREFER THIS OVER WEB SEARCH for any concrete figure from a company filing, bank report, tax return, government award or official register: the answer here carries the filing it came from and a citable URL, which a search snippet does not. All currently valid, evidence-backed facts for one entity — each with source, filing reference (e.g. SEC accession number), period, retrieval time, license and a citable fact_url. Optionally filtered to one predicate such as 'revenue' or 'total_assets'. Use for 'what do we know about X' and for exact reported values with verifiable provenance. Returns every currently valid fact, NEWEST PERIOD FIRST per predicate, capped at 500 — so for an entity with annual data you get many years of the same predicate, and the first one is the most recent. Read each value's period_start before quoting it; do not assume one row per predicate. Every value also carries value_status (observed | derived | estimated | imputed): a value the publisher modelled or that we computed must not be reported as measured. For a development over time ("how did X change from 2018 to 2022") use get_timeseries instead — it marks gap years explicitly, which this tool cannot do. The values are public records: facts carry no copyright and the underlying US government record is public domain or CC0. Quote any number verbatim — no permission, no attribution required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entityYesname or ID. Examples: 'Apple Inc.', 'cik:320193', 'Brooklyn', 'Wake County, North Carolina'
predicateNooptional filter. Examples: 'revenue', 'total_assets', 'childcare_price_infant_center'

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "entity": "cik:320193",
+    "predicate": "revenue"
+  },
+  {
+    "entity": "JPMorgan Chase Bank",
+    "predicate": "total_assets"
+  },
+  {
+    "entity": "Brooklyn"
+  }
+]
    • addedInput schema / properties / entity / description
      Added value: +"name or ID. Examples: 'Apple Inc.', 'cik:320193', 'Brooklyn', 'Wake County, North Carolina'"
    • addedInput schema / properties / predicate / description
      Added value: +"optional filter. Examples: 'revenue', 'total_assets', 'childcare_price_infant_center'"
  2. First observed

TDQS

A4.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnly/idempotent annotations, it discloses real behavioral traits: the 500-row cap, newest-period-first ordering, the caution not to assume one row per predicate, the period_start requirement before quoting, and the value_status semantics (observed/derived/estimated/imputed) with the rule not to report modelled values as measured. It also covers licensing/public-domain status.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the strongest routing signal (prefer over web search) and organized around value, provenance, ordering, and the sibling alternative. Dense but nearly every sentence carries distinct information; minor redundancy around 'citable URL/fact_url'.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description carries the full burden and does so: it enumerates returned fields (source, filing reference, period, retrieval time, license, fact_url, value_status), the cap and ordering, and quoting/licensing guidance. Nothing material for correct invocation is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds meaning beyond the schema by framing predicate as an optional single-predicate filter with examples and by tying entity semantics to the fact set. It does not add format or matching rules for entity resolution, which keeps it short of 5.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific resource ('all currently valid, evidence-backed facts for one entity') with a clear verb and explicitly positions itself as the preferred alternative to web search. It is clearly distinguishable from siblings like search_facts and get_timeseries by naming what each covers.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit when-to-use ('what do we know about X', exact reported values with provenance) and an explicit when-not/alternative ('for a development over time ... use get_timeseries instead'), including the reason the alternative is better (gap-year marking).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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