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Senate Lobbying

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

A4.9/5.0
Behavior5/5

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

Annotations already indicate safe, nondestructive operation. The description adds valuable behavioral details: uses BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation and flagging. No contradiction with 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?

Single paragraph, front-loaded with core action. Every sentence provides essential information: purpose, use case, technical details, and pairing. No wasted words.

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 no output schema, the description explains return format: 'top-N passages with character offsets and similarity scores'. It covers parameters, behavioral constraints, and pairing context. Complete for a search tool with three params.

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 baseline is 3. The description adds value beyond schema: for 'text' it notes max chars and truncation behavior, for 'query' gives natural-language examples. The 'limit' parameter is implicitly described. Slightly exceeds baseline.

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 states the verb 'Semantic search INSIDE a fetched record' and specifies the resource (a fetched record, e.g., SEC 10-K body, article). It distinguishes from siblings by mentioning pairing with ask_pipeworx_grounded. Examples like 'supply-chain risk' further clarify purpose.

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'. Provides advice on pairing with ask_pipeworx_grounded and explains how search_within saves context. No alternative tools are listed but the combination guidance is clear.

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

All Senate Lobbying toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

There are several near-duplicate clusters: ask_pipeworx, ask_pipeworx_beta (explicitly described as currently identical), ask_pipeworx_grounded, deep_research, and validate_claim all route to the same underlying data; the six Polymarket tools (bet_research, arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) heavily overlap in purpose and are easy to confuse. Even detailed descriptions do not fully resolve which tool an agent should pick first.

Naming Consistency3/5

All names use snake_case and many follow a verb_noun pattern (search_filings, get_filing, list_issue_codes, resolve_entity), but there is a mix of verb-first names (ask_pipeworx, compare_entities, generate_llms_txt), noun-first names (recent_changes, entity_profile, pipeworx_trending), and brand-prefixed families (pipeworx_*, polymarket_*). The naming is readable but not a single predictable pattern.

Tool Count2/5

A server named 'Senate Lobbying' exposes 34 tools, but only three (search_filings, get_filing, list_issue_codes) relate to LDA lobbying data. The remaining 31 cover generic Pipeworx data lookup, prediction markets, memory, subscriptions, AI visibility, and npm package audits—an extreme overreach for the apparent scope and likely to confuse an agent expecting a focused lobbying toolkit.

Completeness2/5

For the lobbying domain implied by the server name, only search, get-one-filing, and issue-code enumeration exist; there is no aggregation/stats tool, no lobbyist/client entity resolution for LDA, no registrant or foreign-entity browsing, and no coverage of related concepts like lobbying firm hierarchies or spending trends. The generic Pipeworx tools fill a different domain, so the lobbying-specific surface has significant gaps.

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