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piers-fawkes

Fodda Context Layer

by piers-fawkes
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

search_insights

Read-only

Retrieve expert quotes, editorial analysis, and strategic perspectives from named leaders with source attribution and parent trend context for qualitative research; not raw statistics.

Instructions

Narrative and qualitative evidence layer only: returns expert quotes, editorial analysis, and strategic perspectives from named strategists and industry leaders across all graphs, with source attribution and parent trend context. Does NOT return raw statistics or market sizing — use search_statistics for hard numbers, or get_domain_intelligence for full trends with bundled evidence. When the query names a specific company or brand, brand_tracker is the entry point.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results to return (default: 10, max: 50)
queryYesNatural language search query. E.g. 'expert views on Gen Z luxury' or 'resale market statistics'
typesNoComma-separated evidence types to search: quote, interpretation, signal, metric, or 'all' (default: 'quote,interpretation' — narrative. For hard numbers, use search_statistics or add 'metric').
sectorNoOptional sector focus to filter results (e.g. 'Alcoholic Drinks', 'Non-Alcoholic Drinks', 'Food & Beverage', 'Retail', 'Beauty', 'Sports', 'Technology', 'Luxury Goods'). When provided, bypasses graph IDs and scopes insights extraction to this sector across graphs.
userIdNoOptional user identifier for trial usage tracking.
graph_idNoOptional graph ID to search. If omitted (or when sector is provided), searches across all relevant graphs in parallel. Examples: 'food', 'retail', 'tech', 'travel', 'beauty', 'sports', 'fashion', 'sic', 'pew', 'ce-design'.
min_scoreNoMinimum relevance threshold 0-1 (default: 0.60). Use 0.60 for broad queries, 0.70+ for precise lookups.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.3.3

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description still adds real behavioral context: results carry source attribution and parent trend context, and search spans 'all graphs' in parallel. It stops short of pagination or result-volume behavior, so it is strong but not complete.

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?

Three sentences, no waste, and the positive scope is front-loaded before the exclusions and alternatives. Every clause routes the agent to a decision.

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?

No output schema, but the description conveys what is returned (quotes, analysis, attribution, trend context) and what is not. With annotations covering the safety profile and a 100%-documented schema, nothing an agent needs to call this correctly is missing.

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 every parameter (query, types, sector, graph_id, min_score, limit, userId) is already documented in the schema. The description's mention of 'across all graphs' lightly reinforces the graph_id default, but adds no syntax or format detail beyond the schema. Baseline 3 applies.

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 scope: 'Narrative and qualitative evidence layer only' returning expert quotes, editorial analysis, and strategic perspectives. It distinguishes itself from siblings search_statistics and get_domain_intelligence by name, so an agent can tell exactly which tool this is without opening a schema.

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?

Explicit when-not and alternatives: raw statistics go to search_statistics, full trends with bundled evidence to get_domain_intelligence, and company/brand queries to brand_tracker. The conditions that select each alternative are spelled out rather than left to inference.

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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