tw-market-data

Cite a served number

cite_this

Read-onlyIdempotent

Produce a bibliographic citation for TWMD data — APA, BibTeX, and a re-verifiable token.

★ FOR PAPERS, REGULATORY FILINGS AND ANYTHING A REVIEWER WILL RE-CHECK LATER. A dataset is
corrected, backfilled and re-run. Three years from now a reviewer opening our API sees different
numbers than the
paper, and nobody — author, reviewer, or us — can tell whether the data changed or the author
mis-transcribed. So the citation carries `as_of`, the Merkle `checkpoint_root`, and a
`verify_url`, plus a signed `reproducible_token` that binds those fields TO EACH OTHER.

★ PASS `as_of` IF YOUR WORK IS POINT-IN-TIME. Without it a citation is still produced, but it is
marked `point_in_time: false` and the APA line reads "Retrieved <date>" instead of naming a
knowledge horizon — because re-running the same query later can legitimately return different
figures, and nothing in the data would mark the difference.

★ WHAT IT PROVES: which data was used, and that those fields were signed by us. **NOT that the
figures are correct** — a faithfully committed wrong figure cites and verifies exactly like a
right one. `limitations` says so in the response; keep it when you quote the citation.

Args:
    dataset: the dataset id you queried, e.g. 'valuation'.
    as_of: the knowledge cutoff your work used (YYYY-MM-DD). Omit only for present-day lookups.
    row_key: pin the citation to one specific row, if you are citing one row.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`as_of`	No
`dataset`	Yes
`row_key`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.7/5.0

Behavior5/5

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

The description goes far beyond the annotations' safety hints. It explains the Merkle checkpoint_root, verify_url, signed reproducible_token, the point_in_time: false behavior, and the critical caveat that the citation proves which data was used but not that figures are correct. This is rich behavioral context that materially changes how an agent should interpret the tool's output.

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?

The description is longer than average, but the length is earned: it front-loads the core purpose, then uses bolded headers to explain when to use, what it proves, and what it does not prove. Some prose could be tightened (e.g., the multi-sentence scenario about reviewers in the future), but overall it is well-structured and scannable.

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?

For a tool with only one required parameter and an output schema, the description covers all operational concerns: parameter semantics, point-in-time behavior, verification semantics, and the limitation to preserve. Annotations cover the read-only and idempotent profile. Nothing an agent needs to call this tool correctly is missing.

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

Parameters5/5

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

Schema description coverage is 0%, so the description fully compensates. The Args section explains dataset with an example ('valuation'), as_of with format and omission guidance, and row_key with its conditional use case ('pin the citation to one specific row'). Each parameter gains meaning beyond the bare schema types and titles.

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?

The description states a specific verb and resource: 'Produce a bibliographic citation for TWMD data — APA, BibTeX, and a re-verifiable token.' This clearly distinguishes the tool from siblings like get_inclusion_proof or query_dataset, which serve different purposes. The title 'Cite a served number' further reinforces the narrow scope.

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

Usage Guidelines4/5

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

The description gives explicit when-to-use guidance: 'FOR PAPERS, REGULATORY FILINGS AND ANYTHING A REVIEWER WILL RE-CHECK LATER.' It also explains when to pass as_of ('PASS as_of IF YOUR WORK IS POINT-IN-TIME'). It does not name specific sibling alternatives or provide exclusion criteria, but the intended context is clear enough for an agent to route correctly.

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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TDQS

A4/5.0

Disambiguation4/5

The tools are largely distinct: querying, searching, backtesting, risk reads, alerts, memory, and audit functions each have clear homes. A few adjacent pairs (risk_read vs risk_assess, company_health_check vs positioning_read) could be confused, but the descriptions draw explicit boundaries.

Naming Consistency3/5

There are strong consistent clusters like list_*, get_*, run_*, and memory_*, but the *_read suffix alternates with noun-first names like company_health_check, and bare-verb tools like ask, calendar, chart, and screen break the pattern. The naming is readable but not uniform.

Tool Count2/5

Forty tools is well past the 25+ threshold and makes the surface heavy for an agent to navigate, even though the breadth reflects a genuinely wide platform. Several clusters could plausibly be consolidated without losing capability.

Completeness4/5

The tool surface covers the main lifecycle well: discovery, point-in-time querying, filings search and full text, backtesting, research, risk assessment, alerts, memory, approvals, and provenance verification. Minor gaps exist—no strategy management tools, no memory deletion, no bulk export—but agents can work around them.

Resources

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