Skip to main content
Glama
thierrysays

perplexity-mcp-server

by thierrysays

Perplexity Company News Brief

perplexity_company_news
Read-only

Get a structured, cited news brief for one company: governance, executives, strategic events, financials, and senior HR moves. Specify the company and lookback days to receive five sections with sources.

Instructions

Get a structured, cited news brief on one company: governance, executives, strategic events, financial situation, and senior HR moves.

This is a workflow tool built on top of Perplexity's Sonar chat/completions API: it fixes the prompt structure and section headings so every call returns a comparable, five-section brief, and maps a plain lookback_days number to Perplexity's recency buckets (there is no native "3 months" filter, so lookback_days=90 is mapped to the nearest bucket, 'month').

Args:

  • organization (string): Company name, e.g. "Doctolib".

  • lookback_days (number, 1-365, default 90): how far back to search ("90" = "last 3 months").

  • language ('fr'|'en'): default 'fr'.

  • response_format ('markdown'|'json'): default 'markdown'.

Returns: Markdown: five headed sections (Gouvernance/Governance, Dirigeants/Executives, Événements stratégiques/Strategic events, Situation financière/Financial situation, Mouvements RH/Senior HR moves), each stating "Rien à signaler"/"Nothing to report" if empty, followed by numbered Sources. JSON: { "answer": string (same structured text), "citations": string[], "search_results": [...], "organization": string, "lookback_days": number }

Examples:

  • Use when: "Quelle est l'actualité récente de Nexans ?" -> organization="Nexans"

  • Use when: weekly veille loop over a list of target companies -> call once per company

  • Don't use when: you need sector-wide signals across many companies -> use perplexity_market_signals instead.

Error Handling:

  • Returns "Error: ... Invalid Perplexity API key" if PERPLEXITY_API_KEY is missing/invalid (401).

  • Returns "Error: ... rate limit exceeded" if too many requests (429).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
languageNoLanguage of the synthesized answer.fr
organizationYesLegal or commercial name of the company to research, e.g. 'Doctolib'.
lookback_daysNoHow far back to look for news, in days (default 90 = 'last 3 months'). Mapped internally to Perplexity's nearest recency bucket.
response_formatNoOutput format: 'markdown' for human-readable, 'json' for machine-readable.markdown
Behavior5/5

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

Annotations already declare read-only and non-destructive; the description adds substantial behavioral context: it explains it wraps Perplexity's API, fixes prompt/section structure, maps lookback_days to recency buckets, emits 'Nothing to report' placeholders, and returns specific errors for bad API keys or rate limits. This goes well beyond the annotations and helps predict actual 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 longer than average, but it is organized into clear, labeled sections (Args, Returns, Examples, Error Handling) with no filler. Every section contributes decision-relevant information, and the main purpose is front-loaded in the first sentence.

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?

The tool is a wraper with no output schema, so the description correctly takes on the burden of documenting return values, error behavior, parameter semantics, and usage boundaries. It covers the full range of invocation contexts and likely failure modes.

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

Parameters5/5

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

Although schema description coverage is 100%, the description adds important meaning beyond the schema: it explains that lookback_days is mapped to Perplexity's nearest recency bucket because no native '3 months' filter exists. It also details what each response_format value produces, including the JSON shape and Markdown sections.

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?

The description opens with a specific verb and resource: 'Get a structured, cited news brief on one company,' then lists the exact content areas covered. It also names the sibling tool in the alternatives, making differentiation explicit.

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?

The description gives explicit 'Use when' and 'Don't use when' guidance, including an example user query mapped to the organization parameter. It clearly directs agents to perplexity_market_signals for sector-wide signals instead.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/thierrysays/perplexity-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server