tw-market-data

Search agent memory

memory_search

Read-onlyIdempotent

Recall your own memories — hybrid (semantic + exact-term), with provenance attached.

    Every result carries where it came from (`source_query_ids`, replayable), when it was
    believed (`valid_from`/`valid_to`) and what knowledge time it is about (`as_of`), plus a
    `recall` block stating which model and which filters produced the answer.

    Args:
        query: what you are looking for, in words.
        kinds: restrict to some of 'query' 'factor_def' 'watchlist' 'finding' 'note'.
        as_of: knowledge-time bound — pass a backtest's as_of and nothing recorded later can
            come back. Use this for anything point-in-time.
        key: the stable name, when you know it (e.g. a factor name).
        agent_id: narrow to one of your agents.
        include_superseded: also return old versions (the audit view). Default is current only.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`key`	No
`as_of`	No
`kinds`	No
`limit`	No
`query`	No
`agent_id`	No
`include_superseded`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`hint`	No
`count`	No
`error`	No
`reason`	No
`recall`	No
`blocked`	No
`results`	No
`namespace`	No
`not_investment_advice`	No

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already mark the tool as read-only and idempotent, and the description adds rich behavioral context: results carry source_query_ids, valid_from/valid_to, as_of knowledge-time, and a recall block showing which model and filters produced the answer. It also reveals the current-only default behavior. There is no contradiction with the annotations.

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 well structured: the core behavior is front-loaded, the provenance note is compact, and the Args list is clean and information-dense. Every sentence adds value without repeating the schema.

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?

For a 7-parameter search tool with zero schema descriptions, the description covers behavior, provenance, temporal semantics, defaults, and filtering options, and an output schema exists to cover return shape. The only meaningful gap is the undocumented limit parameter, which leaves its exact cap semantics ambiguous.

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 description coverage is 0%, but the description compensates by explaining six of seven parameters, including the allowed values for kinds and the temporal semantics of as_of. Only limit is left undocumented, so the description does not fully close the parameter-semantics gap.

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 clearly identifies the resource ('your own memories') and the operation (search via 'hybrid semantic + exact-term'), making the tool's purpose unmistakable. The provenance and as-of features further distinguish it from sibling memory tools such as memory_get_watchlist and memory_replay_query.

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 concrete guidance: use as_of for any point-in-time lookup, use key when the stable name is known, use agent_id to narrow scope, and use include_superseded for the audit view. It does not explicitly name alternatives or state when not to use this tool, so it stops short of fully explicit when/when-not guidance.

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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