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Add web finding to live corpus

add_finding

Incorporate web-found mathematical results into the live search corpus for immediate retrieval via BM25 or dense+BM25 when an optional embedding is provided.

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
Behavior5/5

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

The description discloses important behavioral traits beyond annotations: it mentions provenance 'web_added', that BM25 is always used, that dense retrieval is optional if dense_vec is provided, and that no model is loaded by mathlas. This adds value beyond the annotations which only give readOnlyHint, destructiveHint, etc.

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 relatively concise, fitting the purpose and usage into one sentence. It is front-loaded with the main purpose. However, the technical details about dense_vec and BM25 could be slightly more structured, but overall it is 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 (5 parameters, 3 required, optional dense_vec, and outcome of immediate searchability), the description covers all essential aspects: purpose, when to use, parameter meanings, and behavioral consequences. The output schema presumably documents the return value, so the description is complete.

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?

Schema description coverage is 100%. The description adds meaning beyond the schema by clarifying each parameter: 'statement (the real text)', 'slogan (short natural-language denotation)', 'source (URL/arXiv/citation)', and explains dense_vec in detail (must match model, omit for BM25-only). This compensates fully for any ambiguity.

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's purpose: 'Ingest a web-found result into the live mathlas corpus so search_existing_math returns it immediately'. It specifies the verb (ingest), resource (live corpus), and the effect (immediate searchability). This makes it distinct from sibling tools, which focus on searching or other operations.

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 guides usage: 'Use after web-searching per search_directive.' This provides clear context for when to invoke the tool. It doesn't explicitly list when not to use, but the context implies it is for web-found results, and sibling tools cover other scenarios.

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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