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Glama

Twelvedata

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

A5/5.0
Behavior5/5

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

Adds behavioral details beyond annotations: BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation flag, and character offsets for verification.

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?

Concise 4 sentence description, front-loaded with purpose, every sentence adds value, no redundancy.

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?

Covers purpose, usage, technical details, integration with sibling, and return format (passages with offsets and scores) despite no output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

100% schema coverage; description enriches with examples (e.g., SEC 10-K body, query examples), context for text parameter (already fetched), and cap limit.

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, specifies verb and resource, and distinguishes from sibling tools like ask_pipeworx_grounded.

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 advises when to use (when record too large for prompt) and how it pairs with ask_pipeworx_grounded, providing clear alternatives.

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

A3.7/5.0
Disambiguation3/5

While many tools have distinct purposes, there is notable overlap between price, quote, eod, and time_series for price data. Also, the multiple ask_pipeworx variants and deep_research could cause confusion about which to use for factual queries. Some tools like bet_research and polymarket_arbitrage also have overlapping domains.

Naming Consistency4/5

Most tools follow a descriptive snake_case pattern (ai_visibility_check, ask_pipeworx, compare_entities). A few are single words (cryptocurrencies, indices, profile) which is acceptable. No mixing of camelCase or other conventions, so consistent overall.

Tool Count2/5

47 tools is quite high for a single server. While the domain is broad (financial data, prediction markets, SEC filings, etc.), many tools are highly specific (e.g., polymarket_arbitrage, bet_research, scan_dependency) and could be consolidated. The count feels bloated and adds cognitive load.

Completeness4/5

The tool set is impressively comprehensive, covering stocks, forex, crypto, economic data, SEC filings, prediction markets, entity resolution, and even claims validation. Minor gaps exist (e.g., limited drug data despite having some tools), but overall the surface supports a wide range of agentic workflows without obvious missing operations.

Resources