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fd-cn-report

Official

index_records

Bulk-index extracted records into the yearly Elasticsearch index cnreport-{year} for searchable financial report data.

Instructions

Bulk-index extracted records into cnreport-{year}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearYesreport year → determines the index name.
companyNo
recordsYeslist of record dicts (e.g. from ai_extract).
sectionNo
report_idYes
section_idYes
stock_codeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.4

TDQS

C2.6/5.0
Behavior2/5

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

With no annotations, the description carries the full behavioral burden, and it discloses little: it does not say whether the index is created if absent, whether documents are overwritten or upserted, whether the operation is idempotent, or what happens on partial failure in a bulk operation. Only the implicit 'this is a write' is conveyed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

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

The single sentence is front-loaded and wastes no words, but for a 7-parameter bulk mutation it reads as under-specification rather than true conciseness. There is room for prerequisite or behavior detail without bloat.

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

Completeness2/5

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

An output schema exists so return values need not be explained, but this is an un-annotated mutation tool with 29% parameter coverage and no stated prerequisites, permission needs, or failure semantics. An agent lacks enough to invoke it confidently in anything but the simplest case.

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

Parameters2/5

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

Schema description coverage is only 29%, so the description must compensate, and it largely does not. The only parameter context supplied ('year' determines the index name, records come from ai_extract) is already in the schema, while company, section, report_id, section_id, and stock_code carry no meaning in either place.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (index) and resource (records), plus the bulk scope and the derived target index name cnreport-{year}. An agent knows what the call does, but nothing distinguishes it from siblings like delete_index, search_reports, or ai_extract.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit guidance on when to use this versus delete_index, search_reports, or the extraction tools. The phrase 'extracted records' faintly implies a post-ai_extract workflow, but the prerequisite sequence and any exclusions are left to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.