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Glama

Dataverse Harvard

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

A4.6/5.0
Behavior5/5

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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds crucial behavioral context: embedding model (BGE-base-en), similarity measure (cosine), window size (500-char overlapping), character cap (200K with truncation flag), and return format (passages with offsets and scores). 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear front-loaded purpose and logical flow from usage to technical details. It is slightly verbose with implementation specifics (e.g., model name, window size) that could be shortened without losing clarity, 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 no output schema, the description adequately covers return format (passages, offsets, scores), constraints (200K cap), and a pairing suggestion. It addresses the tool's complexity fully, leaving no obvious gaps for an AI agent to misunderstand invocation or output.

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% with all parameters described. The description adds value by explaining the 'text' parameter's maximum length and truncation behavior, providing query examples, and noting the limit default. However, the schema already covers basic descriptions, so the added nuance is moderate.

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, specifying the action ('search within'), the resource ('a fetched record'), and the scope ('INSIDE'). It distinguishes itself from sibling tools like 'search' and 'ask_pipeworx_grounded' by focusing on searching within an already-fetched record, making its purpose unambiguous.

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 use when a record is too large for the prompt and suggests pairing with 'ask_pipeworx_grounded'. However, it does not explicitly state when not to use or list alternative tools for smaller records, leaving some ambiguity.

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

B3.4/5.0
Disambiguation3/5

Several tool pairs have overlapping purposes, notably ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded. Also, remember/recall/forget overlap with general memory operations, and multiple polymarket tools overlap in edge detection. While the descriptions attempt to differentiate, an agent will frequently need to choose between nearly identical tools (e.g., ask_pipeworx vs. ask_pipeworx_beta).

Naming Consistency2/5

Naming conventions are mixed: snake_case (ai_visibility_check, compare_entities), camelCase (ask_pipeworx, generate_llms_txt), and inconsistent verb usage (some start with verbs like 'search', others with nouns like 'dataset'). The polymarket and pipeworx prefixes are helpful, but overall patterns are unpredictable.

Tool Count2/5

With 35 tools, this server has a very large surface area. While the domain is broad (Harvard Dataverse + Pipeworx data + Polymarket), the count feels heavy and includes many near-duplicate tools (ask_pipeworx variants) and niche tools that inflate the total. Many agents would benefit from a smaller, more focused set.

Completeness3/5

The Dataverse subset captures file metadata and search but lacks direct download/upload capabilities, causing dead ends for users who want to access actual data. The Polymarket subset lacks the ability to actually place orders despite extensive edge analysis. The Pipeworx subset covers many data queries but feels unfocused. Overall, there are notable gaps given the stated scope of the server.

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