Extract a named section from a Shanghai Stock Exchange annual-report PDF using the 6-digit ticker, fiscal year, and section title.
Extract a named section from a SSE company's annual-report PDF.
| Name | Required | Description | Default |
|---|---|---|---|
| year | Yes | fiscal year. | |
| section | Yes | section title selector. | |
| ticker_or_name | Yes | 6-digit SSE ticker. |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.3.4Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are supplied, so the description carries the full behavioral burden, and it discloses almost nothing: it doesn't say whether extraction is read-only, whether the section title must match exactly, what happens when a named section is absent, or whether results are cached. The only behavioral hint is that the source is a PDF rather than structured data.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single front-loaded sentence with no filler; the market scope and source type are stated immediately. It is efficient, though arguably too terse to earn a top score.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema present and full parameter documentation, return values and inputs are covered elsewhere, so the description doesn't need to restate them. What is still missing is the operational context that matters for a section extractor: how section titles are matched and what the agent should do when a section isn't present.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% — ticker_or_name, year and section are each documented in the schema — so the baseline is 3. The description adds only marginal meaning, confirming that "section" is a name and that year refers to the annual report, but it does not clarify valid section titles or ticker format beyond what the schema already states.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description gives a specific verb ("Extract"), a specific resource ("a named section"), and constrains both the market (SSE) and the source artifact (annual-report PDF). That scoping cleanly separates it from the exchange siblings get_szse_section, get_bse_section and get_hk_section, though it never names those alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no statement of when to choose this over the near-identical siblings (get_section, extract_section, get_szse_section, get_bse_section, get_hk_section). Usage must be inferred entirely from the word "SSE", and no prerequisites, exclusions, or failure conditions are described.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.