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geoseo-mcp

geoseo-mcp

by Rachit8484
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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perplexity_query

Submit a question to get a Perplexity-generated answer with cited URLs and domains; optionally set the model, token limit, or system prompt.

Instructions

Ask Perplexity (Agent API + web search); return answer + cited URLs/domains.

Defaults to the fast effort preset. Pass model to pin one (e.g. openai/gpt-5-mini); legacy names like sonar still work.

Input Schema

NameRequiredDescriptionDefault
modelNo
questionYes
max_tokensNo
system_promptNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv0.4.0
    • addedInput schema / properties / max_tokens / anyOf
      Added value: +[
+  {
+    "type": "integer"
+  },
+  {
+    "type": "null"
+  }
+]
    • changedInput schema / properties / max_tokens / default
      Previous value: -1024New value: +null
    • removedInput schema / properties / max_tokens / type
      Removed value: -"integer"
  2. First observedv0.3.0

TDQS

B3/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It usefully discloses the execution path (Agent API + web search) and the return shape, but says nothing about cost, latency, rate limits, API-key requirements, or whether results are cached — all material for an external paid query.

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?

Front-loaded with the core purpose and return shape, then parameter hints. Two short sentences with no filler; the parenthetical is compact and earns its place.

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

Completeness3/5

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 needn't be detailed, and the tool is not deeply complex. Still, with no annotations, 0% schema coverage, and two undocumented parameters, the description leaves an agent guessing about token limits and system-prompt behavior.

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 0%, so the description must compensate and it only partly does: it explains 'model' with a concrete example and the 'fast' effort preset default. Neither 'max_tokens' nor 'system_prompt' is mentioned at all, leaving half the parameters undocumented everywhere.

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 and resource ('Ask Perplexity'), names the mechanism (Agent API + web search), and states the return shape (answer + cited URLs/domains). It does not distinguish itself from the many sibling LLM-query tools (openai_query, grok_query, claude_query, multi_llm_query) — it relies on the name alone to convey that choice.

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?

The only conditional guidance is internal to parameters ('defaults to fast effort preset', 'pass model to pin one'). There is no when-to-use, when-not-to-use, or sibling routing advice, which is a real gap given ~8 competing query tools in the sibling list.

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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