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vnklv

Pytch MCP Server

by vnklv

search_journalists

Find verified tech and business journalists by beat, topic, or publication outlet to target media outreach and build contact lists.

Instructions

Search verified tech and business journalists by beat, topic or media publication.

Args: topic: Keyword or beat (e.g. 'AI', 'B2B SaaS', 'Biotech', 'Venture Capital') outlet: Optional media publication filter (e.g. 'TechCrunch', 'Forbes', 'VentureBeat') limit: Max contacts to return (default: 10)

Input Schema

NameRequiredDescriptionDefault
limitNo
topicYes
outletNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the full load, and it discloses the one non-obvious trait: results are limited to 'verified' journalists, implying a curated index. It says nothing about match semantics (exact vs. fuzzy keyword), result ordering, or whether the limit is a hard cap or paginated, which are the behaviors an agent most needs.

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?

The opening sentence is front-loaded and complete, and the Args block earns its space because the schema has zero field descriptions. Slight redundancy remains (limit's default is repeated from the schema), but nothing is padded.

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

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple three-parameter read tool with an output schema covering return values, this is close to sufficient: purpose, all params, and the 'verified' qualifier are stated. The remaining gap is behavioral — ordering, matching, and result-cap semantics — which an agent must guess.

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 0%, so the description must compensate, and it largely does: it explains that topic is a keyword or beat with four concrete examples, that outlet is an optional publication filter with examples, and that limit defaults to 10. It does not say whether topic accepts multiple values or how partial matches are treated.

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+resource ('Search verified tech and business journalists') plus the three filter dimensions (beat/topic, publication, limit). That is enough to distinguish it from the siblings listed (deliverability check, brand visibility, project listing/results), which serve entirely different jobs.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is only implied: an agent can infer this is the lookup step for finding press contacts, but the description states no explicit when-to-use, prerequisites, or what to do if no verified match exists. There is no named alternative because no sibling overlaps, so nothing is actively misleading.

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