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chang0911

丝路E投财务引擎 MCP 适配层

by chang0911

run_uncertainty

Run sensitivity, scenario, and Monte Carlo analysis on project inputs to quantify investment risk. Identify a project by ID or pass five input files and receive downloadable reports with uncertainty results.

Instructions

不确定性分析:引擎真算敏感性/情景/蒙特卡洛+总报告。两种用法:①p_id(+stage) 定位自己的平台项目,交付物落盘用户工作区并返回文件清单;②files 直传5类输入txt(无平台项目号场景,可由 fast_calc_reports 造数闭环产出),成品入平台文件库返回72h下载短链清单(url 可直接交给用户浏览器下载)。不回base64。耗时1~3分钟,超时设≥300秒。前置:需先调 get_protocol_instructions。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
p_idNoE投项目号(纯数字),与 files 二选一
filesNo5类输入txt全文 {文件名:内容},与 p_id 二选一(外部客户端推荐,成品返回72h下载短链)
quickNo快速模式:跳过情景与蒙特卡洛,敏感性粗网格(单因素5点/双因素9×9)+总报告,实测约20~25秒,客户端超时30秒可用;需要精细网格(9点/21×21)与蒙特卡洛时走完整分析(1-3分钟)并加大客户端超时
rangeNo因子扰动幅度%,5~60,默认20;模板要求±5%与±10%两档时,请分别以 range=5、range=10 调用两次
stageNo
factorsNo逗号分隔敏感性因子名单(可选)
mc_samplesNo50~2000,默认400

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does so well: it discloses output destinations (user workspace vs. platform file repository), return artifacts (file list, 72h download short links), an explicit negative (no base64), runtime (1–3 min) and a timeout recommendation (≥300s). It does not cover permissions/auth or error behavior, so it falls short of a perfect score.

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?

Information-dense but front-loaded: the purpose and the two usage modes lead, with timing/prerequisite details trailing. Every sentence carries content, though the packing of many facts into a single block slightly hurts scanability.

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?

For a 7-parameter tool with nested objects and no output schema, the description covers both invocation paths, the required prep call, runtime/timeout, and the returned artifacts, so nothing essential for correct invocation is missing.

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?

Schema coverage is already 86%, so the baseline is 3, but the description adds real meaning: the mutual exclusivity of p_id and files, what each mode returns, and how quick trades scope for speed. It complements rather than merely restating the 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?

States a specific verb and resource (uncertainty analysis computing sensitivity/scenario/Monte Carlo plus an overall report) and explicitly separates the two invocation modes. It even names a sibling, fast_calc_reports, as the producer of inputs, so an agent can route correctly without opening other schemas.

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?

Gives explicit when-to-use for each mode (p_id+stage for your own platform project vs. files direct-upload when there is no platform project number) and states a hard prerequisite (call get_protocol_instructions first). It also explains the closed loop with fast_calc_reports, leaving little to inference.

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