tw-market-data

Read the primary source text

read_primary_text

Read-onlyIdempotent

Read the FULL TEXT of filings and announcements — with proof links and a knowledge cutoff.

★ NOT `search_filings`. That one ranks passages by similarity and hands you fragments; this hands
you whole documents so you can read what was actually said and where it sat in the filing.
Similarity is not importance, and a fragment cannot show you its own context.

★ POINT-IN-TIME: pass `as_of` (YYYY-MM-DD). The cutoff is applied in SQL on the source's declared
knowledge-time column BEFORE the row limit, so a bounded read is a true prefix of what was
knowable, not a random subset of it. **Without `as_of` the read is NOT point-in-time** and says
so in `warnings`.

★ NO SENTIMENT, NO SCORES — deliberately. Judging the text is your job. A stored score is one
model's output on one day; after that model changes, the stale number still sits in the table
looking exactly like a fresh one.

★ READ `corpus_reality` BEFORE CONCLUDING ANYTHING. The full-text corpus is SMALL and the
response says how small. One source carries ~1M rows of TITLES ONLY — a large row count there is
breadth, not depth, and "what did they say about it" is not answerable from titles.

★ A ticker that returns nothing appears in `coverage.missing`. That means nothing is held for it
under those filters — NOT that the company disclosed nothing. Do not fill the gap.

Args:
    source: which corpus, e.g. 'announcements_fulltext' or 'mops_major_event'.
    tickers: restrict to these codes, e.g. ['2330'].
    as_of: knowledge cutoff (YYYY-MM-DD).
    since: optional lower bound on the same knowledge-time column.
    limit: max documents (these are whole documents; keep it small).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`as_of`	No
`limit`	No
`since`	No
`source`	Yes
`tickers`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A5/5.0

Behavior5/5

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

Even with readOnlyHint, openWorldHint, and idempotentHint annotations, the description adds significant behavioral context: the SQL cutoff is applied before the row limit, `as_of` absence disables point-in-time semantics and warns, the tool deliberately omits sentiment/scores, and the corpus may be small or title-only. It fully discloses caveats the agent needs to interpret results safely.

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?

The description is long but every section earns its place: it opens with the core purpose, then gives alternative routing, point-in-time mechanics, anti-patterns, corpus caveats, and parameter semantics. Bolded lead-ins and bullet-like organization make it scannable, and there is no redundant or filler content.

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 read-only tool with an output schema available, the description covers all necessary call decisions: what it returns conceptually, how to keep reads bounded, what caveats affect interpretation, and how to handle missing results. The presence of an output schema reduces the need to describe return fields, and the description fills all other gaps around usage and interpretation.

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 carries the full burden, and it succeeds. Every parameter is given meaning and format: `source` with examples, `tickers` with example values, `as_of` as YYYY-MM-DD knowledge cutoff, `since` as lower bound on the same column, and `limit` with a warning to keep it small because these are whole documents.

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: read the full text of filings and announcements, with proof links and a knowledge cutoff. It explicitly distinguishes itself from search_filings by contrasting whole documents vs fragments. This makes the tool's purpose unambiguous and differentiates it from a closely related sibling.

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?

The description explicitly names `search_filings` as the alternative and explains why this tool should be used instead when full text is needed. It also provides clear conditions: point-in-time behavior requires `as_of`, read `corpus_reality` before concluding anything, and a missing ticker means nothing is held, not that no disclosure occurred. This is thorough when-to-use and when-not-to-use guidance.

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

All tw-market-data tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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

Need Help?