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Openstates

by io.github.pipeworx-io

Search Within a Source

search_within
Read-onlyIdempotent

Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe document text to search inside (max ~200K chars).
limitNoMax passages to return (1-20, default 5).
queryYesNatural-language query — what passages do you want? E.g. "supply-chain risk", "fiscal year 2024 revenue", "drug interactions with warfarin".

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive, but the description adds substantial behavioral detail: returns character offsets and similarity scores, uses BGE-base-en embeddings with cosine over 500-char windows, and has a 200K character cap with truncation flagged. This is transparency beyond annotations, not a contradiction.

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 two sentences but every sentence earns its place: purpose, use case, pairing, technical details, and limits. It is front-loaded with the core action and structured logically with dashes and a semicolon. Minor redundancy ('saves context' and 'returns only the passages that matter' convey similar ideas) prevents a 5.

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?

Despite lacking an output schema, the description explicitly covers return values (passages, offsets, scores), the max input cap and truncation behavior, and the integration pattern with a sibling tool. It fully compensates for the missing output schema and provides a complete operational picture.

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 coverage is 100%, so the baseline is 3. The description adds real-world context: examples of queries ('supply-chain risk', 'fiscal year 2024 revenue'), clarifies text as 'already pulled' content, and mentions output offsets. While the schema already defines parameters well, the description enriches meaning with practical examples, justifying a 4.

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 opens with a specific verb+resource+scope: 'Semantic search INSIDE a fetched record.' It clearly distinguishes from siblings by naming ask_pipeworx_grounded and explaining the difference ('ground over the relevant passages instead of the whole document'). The purpose is unmistakable and context-rich.

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?

Explicitly states when to use: 'Use when the record is too big to cram into the prompt.' It also provides a pairing workflow with ask_pipeworx_grounded, which serves as both a when-to-use and a when-not-to-use alternative. This is exemplary 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

B3.1/5.0
Disambiguation1/5

The tool set includes multiple pairs of nearly identical tools (e.g., ask_pipeworx and ask_pipeworx_grounded, bet_research and polymarket_arbitrage) that overlap heavily in purpose. Many tools also combine unrelated functions, making it difficult for an agent to select the right one without confusion.

Naming Consistency1/5

Tool names follow no discernible pattern: snake_case (ai_visibility_check), verb_noun (ask_pipeworx, get_bill), and even lengthy descriptive names (scan_competitor_ai_presence) are mixed. The naming style is chaotic and inconsistent across the set.

Tool Count2/5

With 30 tools, the count is excessive for a server supposedly focused on OpenStates (state legislatures). Only a few tools (search_bills, get_bill, etc.) relate to the server's name, while the rest are tangentially related to data lookups, betting, or AI visibility, making the set bloated.

Completeness1/5

The server's core domain (state legislative data) is severely underserved: only about 5 tools cover bills and legislators, lacking basic CRUD operations like create, update, or delete. Many obvious operations (e.g., searching bills by subject, tracking votes) are missing, while unrelated tools dominate.

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