tw-market-data

Get a code example

get_code_example

Read-onlyIdempotent

Emit a copy-pasteable HTTP snippet wired to the real endpoint, header and parameter names.

★ FOR WRITING YOUR OWN CLIENT, NOT FOR GETTING DATA. Every other tool here answers a question;
this one hands you source code so your program can ask it directly over HTTPS. Nothing is
fetched and no rows come back.

★ THE CONSTANTS ARE READ OUT OF THE SERVER, NOT REMEMBERED. Base URL, the `X-API-Key` header
spelling, and the route path all come from the code that serves them. The worst kind of broken
example is one that looks right: a mistyped path 404s and the reader blames their own key.

★ AN INTENT IT HAS NO ROUTE FOR IS REFUSED. It will not point at a plausible-looking path it
has not confirmed. Python output is passed through Python's own compiler before it is returned;
the JavaScript variant is structure-checked only, and says so.

⚠️ The credential in the snippet is an obvious placeholder, never a realistic-looking string —
a convincing fake gets pasted, sent, and then fails somewhere nobody can trace.
Args: intent, language (python/javascript), ticker, optional as_of.

Input Schema

TableJSON Schema

Name	Required	Default
`as_of`	No
`intent`	Yes
`ticker`	No	2330
`language`	No	python

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already mark it read-only/idempotent/non-destructive, and the description adds substantial extra behavior: constants are read live from the server, unconfirmed routes are refused, Python output is compiler-checked, JS is structure-checked only, and credentials are obvious placeholders. This is exactly the kind of context that prevents misuse.

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

Conciseness5/5

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

Main purpose is front-loaded, and each starred paragraph earns its place by addressing a distinct operational concern. It is longer than minimal, but the length is justified by the need to prevent plausible-looking broken client code.

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

Completeness4/5

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

The description thoroughly covers behavior and edge cases, and an output schema exists, so return structure need not be spelled out. The only notable gap is the meaning and allowed values of the required 'intent' parameter, which an agent would need clarified to reliably invoke the tool.

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

Parameters3/5

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

The schema has no descriptions (0% coverage), so the description must carry parameter meaning. It lists 'intent, language (python/javascript), ticker, optional as_of' and clarifies language options and as_of optionality, but it never defines what an 'intent' is or what format as_of should take. This is partial compensation, not full.

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?

Description opens with a specific verb and resource: 'Emit a copy-pasteable HTTP snippet wired to the real endpoint, header and parameter names.' It also explicitly distinguishes this tool from siblings ('FOR WRITING YOUR OWN CLIENT, NOT FOR GETTING DATA'), so an agent can select it confidently.

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?

It states when to use this tool ('if your program needs to ask over HTTPS') and contrasts it with all other tools ('Every other tool here answers a question; this one hands you source code'). No ambiguity remains about whether to use this for data retrieval.

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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