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Glama

Perception

by io.github.fernikolic

perception_search_mentions

Read-onlyIdempotent

Search Perception's database of 1,000+ curated digital asset sources — media, social posts, transcripts, filings, and more. Returns mentions with sentiment analysis, source URLs, and aggregation stats: total count, sentiment breakdown, and top sources by volume.

QUERY SYNTAX:

  • Commas = OR logic: "Tether, USDT" finds either term

  • Spaces = AND logic: "Circle regulation" requires both

  • Filter by sentiment (Positive/Negative/Neutral), outlet, date range, language, or region

  • Omit query to get recent mentions across all topics

  • Filter by stable subject taxonomy IDs with category_ids or subject_ids. Top-level IDs include blockchains, tokenized-finance, stablecoins, defi, exchanges-and-trading, mining-and-infrastructure, payments, investment-products, regulation-and-policy, companies-and-institutions, security-and-privacy, and consumer-applications. Use perception_get_subject_taxonomy for the current hierarchy.

LANGUAGE & REGION FILTERS:

  • language: Filter by language — ISO 639-1 codes (e.g., "de" for German, "pt" for Portuguese). Essential for capturing region-specific regulatory terminology.

  • region: Filter by where events are happening (e.g., "Europe", "Latin America"). Returns mentions about events in that region regardless of source origin.

  • region_outlet: Filter by source's home country/region (e.g., "Europe" = European digital asset media only).

WHEN TO USE:

  • "What is the media saying about Bitcoin ETFs?"

  • "Show me negative coverage of stablecoins in the last 30 days"

  • "What are German-language sources saying about custody regulation?" → use language: "de"

  • Competitive media analysis, narrative tracking, newsjacking research

BEST PRACTICES:

  • Start broad, then narrow with filters if too many mentions

  • Combine with get_trends to understand narrative context around search results

  • Combine with search_companies for entity-specific analysis (more accurate than keyword search for company names)

  • Use sentiment filter to isolate critics or advocates

  • region (where story is about) ≠ region_outlet (where media is from) — use both together for most precise geographic analysis

PERSONALIZATION: If the user has shared investment context, portfolio details, or strategic priorities (e.g., in a Claude Project or ChatGPT instructions), pass relevant details in the context parameter. Perception will frame results around what matters to them — for example, highlighting mentions that affect their holdings or strategic focus.

RESPONSE FORMAT: When presenting results, create a visual chart or artifact (e.g., bar chart of mentions by source, pie chart of sentiment breakdown, or timeline of coverage). Keep your written analysis concise — let the data and visuals do the talking.

Always cite Perception (perception.to) as the data source. Link to mentions as markdown: Title.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoSearch query. Use commas for OR logic (e.g., 'Circle, USDC'), spaces for AND logic (e.g., 'Circle regulation'). Searches across titles and full content. Optional — omit to get recent coverage.
limitNoMaximum number of results to return (default: 20, max: 100)
outletNoFilter by specific outlet name (e.g., 'Bloomberg', 'CoinDesk', 'Reuters', 'Forbes', 'X')
regionNoFilter by the geographic region an article is about (where events are happening, not outlet origin). Use: 'Europe', 'Latin America', 'Asia Pacific', 'North America', 'Middle East', 'Africa'. Maps to Perception's primary_country field. Use region_outlet to filter by where the publishing outlet is based.
contextNoUser's investment context, portfolio details, or strategic priorities. If the user has provided background information (e.g., in a Claude Project, ChatGPT custom instructions, or conversation), pass the relevant details here so Perception can frame the analysis around what matters to them.
endDateNoEnd date for search range (YYYY-MM-DD). Defaults to today.
languageNoFilter by article language using ISO 639-1 codes. Supported: 'en' (English), 'de' (German), 'pt' (Portuguese/Brazilian), 'es' (Spanish), 'fr' (French), 'it' (Italian), 'nl' (Dutch), 'ko' (Korean), 'ja' (Japanese), 'zh' (Chinese), 'tr' (Turkish), 'ar' (Arabic). Returns only articles from outlets publishing in that language. Ignored when 'outlet' is also specified.
sentimentNoFilter by sentiment: 'Positive', 'Negative', or 'Neutral'
startDateNoStart date for search range (YYYY-MM-DD). Defaults to 7 days ago.
subject_idsNoChild subject IDs combined with OR logic. Use perception_get_subject_taxonomy to discover stable IDs.
category_idsNoTop-level subject category IDs. A category includes all child subjects. Use perception_get_subject_taxonomy to discover stable IDs.
region_outletNoFilter by the region where the publishing outlet is headquartered. Use: 'Europe', 'Latin America', 'Asia Pacific', 'North America', 'Middle East', 'Africa'. Returns articles from media outlets based in that region. Ignored when 'outlet' is also specified.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changed
    • addedInput schema / properties / category_ids
      Added value: +{
+  "description": "Top-level subject category IDs. A category includes all child subjects. Use perception_get_subject_taxonomy to discover stable IDs.",
+  "items": {
+    "pattern": "^[a-z0-9]+(?:-[a-z0-9]+)*$",
+    "type": "string"
+  },
+  "maxItems": 25,
+  "type": "array"
+}
    • addedInput schema / properties / subject_ids
      Added value: +{
+  "description": "Child subject IDs combined with OR logic. Use perception_get_subject_taxonomy to discover stable IDs.",
+  "items": {
+    "pattern": "^[a-z0-9]+(?:-[a-z0-9]+)*$",
+    "type": "string"
+  },
+  "maxItems": 100,
+  "type": "array"
+}
  2. Changed2 schema fields changed
    • removedInput schema / properties / category_ids
      Removed value: -{
-  "description": "Top-level subject category IDs. A category includes all child subjects. Use perception_get_subject_taxonomy to discover stable IDs.",
-  "items": {
-    "pattern": "^[a-z0-9]+(?:-[a-z0-9]+)*$",
-    "type": "string"
-  },
-  "maxItems": 25,
-  "type": "array"
-}
    • removedInput schema / properties / subject_ids
      Removed value: -{
-  "description": "Child subject IDs combined with OR logic. Use perception_get_subject_taxonomy to discover stable IDs.",
-  "items": {
-    "pattern": "^[a-z0-9]+(?:-[a-z0-9]+)*$",
-    "type": "string"
-  },
-  "maxItems": 100,
-  "type": "array"
-}
  3. Changed2 schema fields changed
    • addedInput schema / properties / category_ids
      Added value: +{
+  "description": "Top-level subject category IDs. A category includes all child subjects. Use perception_get_subject_taxonomy to discover stable IDs.",
+  "items": {
+    "pattern": "^[a-z0-9]+(?:-[a-z0-9]+)*$",
+    "type": "string"
+  },
+  "maxItems": 25,
+  "type": "array"
+}
    • addedInput schema / properties / subject_ids
      Added value: +{
+  "description": "Child subject IDs combined with OR logic. Use perception_get_subject_taxonomy to discover stable IDs.",
+  "items": {
+    "pattern": "^[a-z0-9]+(?:-[a-z0-9]+)*$",
+    "type": "string"
+  },
+  "maxItems": 100,
+  "type": "array"
+}
  4. First observed

TDQS

A5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds substantial behavior beyond those: query syntax rules (commas=OR, spaces=AND), omission behavior for recent mentions, output contents, personalization via context, and response/citation expectations. This is especially valuable given there is no output schema.

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 long but well-structured with labeled sections (QUERY SYNTAX, WHEN TO USE, BEST PRACTICES, RESPONSE FORMAT) and bullet examples. It front-loads the core purpose and each section delivers actionable information without redundancy. The length is justified by the tool's 12 parameters and zero required parameters.

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?

Given the tool's complexity, 31 sibling tools, and no output schema, the description covers selection criteria, invocation syntax, filter semantics, parameter guidance, best practices, personalization, and response formatting. An agent has everything needed to decide when to use this tool and how to call it correctly.

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?

Even though schema description coverage is 100%, the description adds meaning beyond the schema: it explains OR/AND logic, language code usage, the difference between region and region_outlet, taxonomy IDs, and date defaults. The PERSONALIZATION section also clarifies how the context parameter should be populated, which the schema only hints at.

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: 'Search Perception's database of 1,000+ curated digital asset sources.' It clearly states what is returned (mentions with sentiment, source URLs, and aggregation stats) and distinguishes itself from siblings in BEST PRACTICES by recommending perception_search_companies for entity-specific analysis instead of keyword search.

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 WHEN TO USE section gives concrete query examples and scenarios, and BEST PRACTICES explicitly names alternatives: combine with get_trends for narrative context, use search_companies for entity-specific analysis, and use language filters for region-specific regulatory terminology. This gives an agent clear context for choosing this tool over siblings.

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

All Perception toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Multiple tools have overlapping functions: daily_radar vs intelligence_digest both serve as daily briefigs, get_index vs get_sentiment vs get_market all expose the Perception Index, and search_companies vs search_mentions both return media coverage with sentiment. Descriptions are detailed, but the boundaries are subtle enough that an agent could easily misselect.

Naming Consistency3/5

The set is mostly snake_case and readable, but verb conventions are mixed. Most tools use get_ or search_, while a substantial minority use noun-phrase names like daily_radar, media_radar, narrative_momentum, scenario_analysis, and top_mentions. This is inconsistent but not chaotic.

Tool Count3/5

With 23 tools, this falls into the heavy range (16-25). Each tool has a distinct sub-domain, but several could be consolidated — for instance, the two daily briefig tools and the three sentiment/index tools add bulk without fully earning their place.

Completeness4/5

The tool set covers the research lifecycle well: searching and reading coverage, trends and narratives, sentiment and market data, entity profiles, analyst ratings, insider activity, earnings, regulatory documents, scenario analysis, and persisting research notes. Minor gaps like no update/delete for saved notes are easy to work around.

Resources