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Add web finding to live corpus

add_finding

Add a web-found mathematics result to the live Mathlas corpus for immediate retrieval via search. Optionally include a dense vector for full dense+BM25 search.

Instructions

Ingest a web-found result into the live mathlas corpus so search_existing_math returns it immediately (provenance 'web_added'; BM25 always — no model load; full dense retrieval too if you pass dense_vec embedded in the served index's space). Use after web-searching per search_directive. Args: statement, slogan, source, optional name, optional dense_vec.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
statementYesthe web-found result's statement (the real text)
sloganYesa short natural-language denotation of it (what it says)
sourceYeswhere it came from: a URL / arXiv id / citation
nameNooptional name/title of the result
dense_vecNoOPTIONAL dense embedding of the slogan, computed BY YOU (the AI) with the SAME model the served index uses, length == the served index dim. Storing it gives the finding full dense+BM25 retrieval (found even when wording differs from the query). NO model is loaded by mathlas. Omit for BM25-only.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYes
statementNo
nameNo
sloganNo
sourceNo
provenanceNo
dense_addedNo
n_findingsNo
noteYeson failure (e.g. dense_vec dim mismatch) says exactly what to fix; the finding is NOT added
Behavior3/5

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

No annotations provided, so description carries full burden. Discloses BM25 always, no model load, optional dense retrieval, and provenance 'web_added'. However, does not mention duplicate handling, authorization needs, or side effects. Adequate but could be more thorough.

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 well-structured sentences with an args list. Key information front-loaded: immediate availability, retrieval modes, usage directive. No superfluous text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 5 parameters (3 required) and an output schema (exists but not shown), description covers purpose, when to use, parameter details, and behavioral traits. Does not explain return values or handle edge cases like duplicates, but overall sufficient for an AI agent.

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?

With 100% schema description coverage, baseline is 3. Description enriches meaning: clarifies 'statement' as real text, 'slogan' as natural-language denotation, 'source' as URL/arXiv/citation, and explains dense_vec computation and effect. Adds value beyond schema.

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?

Description clearly states it ingests web findings into the live mathlas corpus for immediate retrieval via search_existing_math. Distinct verb 'Ingest' and resource 'live mathlas corpus' differentiate from sibling tools like search_existing_math (retrieval) and search_directive (search).

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?

Explicitly says 'Use after web-searching per search_directive', providing clear context for when to invoke. Does not explicitly state when not to use or detail alternatives, but the directive is sufficient for an AI agent.

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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