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Glama

Gong

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. Added

TDQS

A4.4/5.0
Behavior5/5

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

Annotations indicate read-only, open-world, idempotent, and non-destructive behavior; the description adds substantial detail: uses BGE-base-en embeddings, cosine similarity over 500-char overlapping windows, 200K char cap with truncation flag, and character offsets in results. No contradictions; transparency is high.

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 well-structured with a clear, front-loaded first sentence. It covers purpose, usage, and technical details in about 6 sentences without redundancy. Slightly verbose in some areas (e.g., repeating the pairing advice), but overall efficient.

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?

Given the tool's complexity (3 parameters, no output schema) and rich annotations, the description fully covers what the tool does, how it works (embedding details, truncation, offsets), and when it should be used. It also addresses integration with a sibling tool. No gaps detected.

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 coverage is 100%, so baseline is 3. The description does not add significantly beyond the schema: it reiterates the max chars for text, default and range for limit, and the nature of the query. No additional depth or nuance is provided.

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 tool performs semantic search inside a fetched record. It specifies the resource (the text parameter) and action (return top-N passages with character offsets and similarity scores). It distinguishes itself from sibling tools by mentioning pairing with ask_pipeworx_grounded and contrasting with fetching the whole document.

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 explicitly advises using the tool when the record is too large for the prompt and highlights the context-saving benefit. It also mentions a complementary tool (ask_pipeworx_grounded). Lacks explicit when-not-to-use guidance, but context is clear enough.

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

A3.9/5.0
Disambiguation4/5

Most tools have distinct purposes due to detailed descriptions, but there is potential confusion among the many Polymarket and pipeworx-related tools. The Gong-specific tools are clearly separated.

Naming Consistency3/5

Naming conventions are mixed: some use snake_case, others camelCase, and there is inconsistency between groups (e.g., gong_* vs. polymarket_*). However, within each subgroup, naming is consistent.

Tool Count2/5

35 tools is high for coherence. The server covers multiple domains (Gong calls, data research, betting), leading to an overloaded toolset that could be streamlined into fewer, more general tools.

Completeness4/5

The toolset is comprehensive for its intended use cases, covering Gong call management, a wide array of data lookups, and Polymarket betting analysis. Minor gaps exist, such as limited CRM features beyond calls.

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