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Glama

Sec Regulations

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".

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already indicate safe, idempotent, non-destructive read. Description adds model details (BGE-base-en, cosine, 500-char windows), character limit with truncation, and output structure with offsets. No contradictions.

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?

Two sentences with clear structure: purpose first, then usage, then technical details. No unnecessary words; every sentence adds value.

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, description fully explains return format (passages with offsets and similarity scores). Covers all important aspects: input, behavior, constraints, and output for a search tool.

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 3. Description adds practical examples for query (e.g., 'supply-chain risk'), clarifies text max chars, and explains limit default. These enhance schema's descriptions.

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?

Clearly states 'Semantic search INSIDE a fetched record' with specific verb and resource. Distinguishes from sibling ask_pipeworx_grounded by describing how they pair together.

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 tells when to use: 'when the record is too big to cram into the prompt' and mentions pairing with ask_pipeworx_grounded. No alternatives needed beyond that.

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
Disambiguation2/5

Several tools have nearly identical purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical, polymarket_arbitrage and polymarket_edges both scan for betting opportunities, and ai_visibility_check overlaps with scan_competitor_ai_presence. The long descriptions help, but an agent must read carefully to distinguish the meta-routers (ask_pipeworx, deep_research, discover_tools, suggest_questions) from each other.

Naming Consistency4/5

The overwhelming majority use lowercase snake_case with a verb-first or domain-prefixed noun pattern (ask_pipeworx, sec_regulations_search, polymarket_edges). Minor deviations like entity_profile, recent_alerts, and ai_visibility_check are noun-first, and sec_regulation vs sec_regulations_search is slightly inconsistent, but the overall style is predictable.

Tool Count2/5

At 33 tools this exceeds the 25-tool threshold for 'too many', and most are unrelated to the server's stated name 'Sec Regulations'—only sec_regulation and sec_regulations_search fit that label. While each tool has a defined purpose, the set feels like several distinct servers (research, betting, memory, subscriptions) crammed into one.

Completeness3/5

For a general data-research platform, the surface is broad: Q&A routers, entity resolution/profiles, comparisons, validation, memory, subscriptions, and domain-specific tools. But the SEC-regulation focus implied by the server name is severely under-served, and there's no direct tool to fetch a raw document by citation/URI outside of the ask_pipeworx router. The Polymarket cluster is over-built relative to other data domains.

Resources