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search_sec_filings

SEC EDGAR filings processed with an LLM to extract supply chain signals, sentiment, and material mentions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFull-text search (ranked) across entity name, summary, materials, and companies.
formNoSEC form type.
limitNolimit (default 50, max 200)
sinceNoISO date; filings on or after.
entityNoCompany / filer name (partial match).
offsetNooffset (default 0)
sectorNoFilter to a tech vertical (quantum = quantum-relevant filings). Omit for all.
materialNoMaterial mentioned in filing (synonym-expanded).
sentimentNoSupply chain sentiment.
importanceNostrategic_importance level.

TDQS

B3.1/5.0
Behavior3/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It adds a useful behavioral trait: filings are pre-processed with an LLM to produce supply chain signals, sentiment, and material mentions, rather than returning raw filings. However, it does not mention rate limits, permissions, pagination behavior, or whether results are read-only, leaving significant gaps for a search tool.

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 a single compact sentence with no filler or repetition. It front-loads the core resource (SEC EDGAR filings) and the value-add (LLM processing for supply chain signals). While it is short relative to the tool's complexity, it is not bloated and earns its place.

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

Completeness2/5

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

This is a complex tool with 10 parameters, no output schema, and no annotations. The one-sentence description is insufficient: it does not explain how the fields combine (e.g., are filters ANDed?), what the returned data structure looks like, or how to interpret the LLM-extracted signals. The rich parameter schema compensates for filter meanings but not for overall tool behavior and result expectations.

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?

Schema description coverage is 100%, so the baseline is 3. The description does not add parameter-specific details beyond what the schema already provides, though it introduces the key concepts (supply chain signals, sentiment, material mentions) that map to the `sentiment` and `material` parameters. This adds minimal semantic value but does not compensate for any missing schema info.

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

Purpose4/5

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

The tool name 'search_sec_filings' and description identify the resource (SEC EDGAR filings) and the function (extract supply chain signals, sentiment, and material mentions via LLM processing). It is clearly distinct from sibling tools that cover funding, trade flows, mineral deposits, and regulatory signals. However, the description itself does not explicitly state the verb 'search' or 'query', relying on the tool name to imply the action.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives such as search_regulatory_signals or search_mineral_deposits. There is no mention of exclusions, prerequisites, or complementary tools. Usage context is only implied by the resource type (SEC filings), which is weak guidance for an agent choosing among sibling search tools.

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

A3.8/5.0
Disambiguation4/5

Each tool targets a distinct data source (funding, trade, minerals, regulations, entities, filings), and the descriptions clarify their unique purposes. However, search_regulatory_signals and search_restricted_entities have conceptual overlap around compliance/restrictions, so an agent could occasionally confuse them without careful reading.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (get_quantum_funding, get_trade_flows, search_*). The use of 'get' versus 'search' is a meaningful distinction between direct retrieval and query-based lookup, so the naming remains uniform and clear.

Tool Count5/5

Six tools is well within the ideal range for a specialized data server. Each tool covers a distinct aspect of semiconductor supply chain data without redundancy, making the count feel appropriately scoped and manageable.

Completeness4/5

The set covers upstream resources, trade, regulatory signals, entity screening, and market intelligence, which is strong for the domain. The main gap is that get_quantum_funding is specific to quantum technology rather than the broader semiconductor space, leaving general funding data unaddressed.

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