Inferventis MCP Server

platform_tool_finder

Discovers the most relevant tools available on this MCP server for a given task using local semantic search (MiniLM-L6-v2 embeddings). Accepts a plain-English description of what needs to be accomplished and returns the best matching tools ranked by relevance, along with their input schemas, pricing tier, and exact call instructions. Use this tool first when you are connected to this server but do not know which specific tool to call — describe your goal and let platform_tool_finder identify the right capability. Do not use this tool if you already know the tool name — call that tool directly instead. Returns up to 10 results ranked by semantic similarity score.

Input Schema

TableJSON Schema

Name	Required	Description
`top_k`	No	How many ranked results to return. Integer between 1 and 10. Defaults to 3. Use a higher value when the best tool is ambiguous.
`intent`	Yes	Plain-English description of what you need to accomplish. Be specific about the goal, not the tool name. Example: 'I need to convert 500 dollars to euros at the current exchange rate' or 'get the latest technology news headlines'.
`model_hint`	No	Optional: the name of the calling LLM model. Examples: claude, gpt-4, gemini. Used to apply model-specific manifest variant ranking when available. Omit if unknown.

TDQS

A4.5/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses the search technique (MiniLM-L6-v2 embeddings), input format, output contents (ranked tools, schemas, pricing tier, call instructions), and result limit. It does not mention edge cases like no matches, but overall it is transparent about behavior.

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?

The description is appropriately sized at four sentences, front-loaded with the core purpose, then usage guidance, alternatives, and output details. Every sentence adds value with no redundancy.

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?

Despite no output schema, the description explains exactly what is returned (best matching tools, ranked, with schemas, pricing tier, and call instructions) and states result limit. It covers intent, usage, and output, making it complete for a tool-finder with 3 straightforward parameters.

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

Parameters3/5

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

Schema description coverage is 100%, so baseline is 3. The description adds no parameter-level detail beyond the schema. The schema already explains intent, top_k, and model_hint with examples and defaults, so the description does not need to compensate.

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 uses a specific verb ('Discovers') with a clear resource ('most relevant tools available on this MCP server') and mechanism ('local semantic search'). It explicitly distinguishes itself from the domain-specific sibling tools by positioning itself as a meta-tool for discovering those tools.

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?

Description provides explicit when-to-use guidance ('Use this tool first when you are connected to this server but do not know which specific tool to call') and when-not-to-use ('Do not use this tool if you already know the tool name — call that tool directly instead'). This fully clarifies alternatives.

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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TDQS

A3.9/5.0

Disambiguation2/5

Multiple tools have overlapping purposes, particularly the five fiat FX tools (currency_convert, currency_convert_lite, currency_convert_open, currency_fx_lite, currency_rates) and the three crypto tools (crypto_fx_rates, crypto_price, crypto_price_lite). Although descriptions attempt to differentiate them by source or detail level, the boundaries are subtle enough that an agent could easily misselect.

Naming Consistency3/5

Tool names use a mix of noun_noun, noun_verb, and adjective_noun patterns. Some include a 'lite' suffix consistently, but others like 'finnhub_stock_quote' and 'stripe_payment_records' have vendor prefixes, while bank tools lack them. Overall, the naming is readable but lacks a single consistent pattern.

Tool Count3/5

At 20 tools, the server covers a broad scope (finance, news, web, timezone) but includes redundancy (e.g., five FX conversion tools, two financial calculators). The count is not excessive for a general utility server, but it feels slightly bloated due to multiple near-identical variants.

Completeness3/5

The tool set covers many common financial and informational needs, but notable gaps exist: no historical stock data, no support for non-Stripe payment processors, and no ability to initiate payments (read-only). The news sources are limited to BBC and Guardian. Some areas are over-covered while others are missing, resulting in moderate completeness.

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