Skip to main content
Glama

Perception

by io.github.fernikolic

perception_get_article

Read-onlyIdempotent

Get the full text of a specific source by its URL. Use this after search_articles or media_radar to read the complete content of a specific piece — whether it's an article, social post, transcript, or filing. Returns the full body, outlet, author, publication date, and sentiment.

WHEN TO USE:

  • User wants to dig into a specific result from search

  • Need full context for detailed analysis or summarization

  • For general analysis, content previews from search_articles are usually sufficient — only use this for deep dives

Always link to the original article: Title. Cite Perception (perception.to) as the data source.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe exact URL of the source to retrieve. Use a URL from a previous search_articles or media_radar result.
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.
publication_dateNoPublication date in YYYY-MM-DD format from the search result. Include it when available so retrieval can query the exact date partition.

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / properties / publication_date
      Added value: +{
+  "description": "Publication date in YYYY-MM-DD format from the search result. Include it when available so retrieval can query the exact date partition.",
+  "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
+  "type": "string"
+}
  2. Changed1 schema field changed
    • removedInput schema / properties / publication_date
      Removed value: -{
-  "description": "Publication date in YYYY-MM-DD format from the search result. Include it when available so retrieval can query the exact date partition.",
-  "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
-  "type": "string"
-}
  3. Changed1 schema field changed
    • addedInput schema / properties / publication_date
      Added value: +{
+  "description": "Publication date in YYYY-MM-DD format from the search result. Include it when available so retrieval can query the exact date partition.",
+  "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
+  "type": "string"
+}
  4. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnlyHint=true, idempotentHint=true, destructiveHint=false), so the description's job is to add behavioral context beyond that. It does so by disclosing the exact return fields (full body, outlet, author, publication date, sentiment) and the recommended worklow after search. It doesn't mention failure modes like missing URLs, but this is not a significant gap for a read-only retrieval tool.

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 description is well-structured: a crisp opening sentence, a WHEN TO USE block, and a final formatting directive. It is slightly longer than strictly necessary, but every section earns its place by carrying distinct guidance. The key purpose is front-loaded and the supporting instructions are logically ordered.

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?

Given no output schema, the description compensates by naming the returned fields. It also covers when to use the tool, how to source the URL, and how to cite the result. The only absence is explicit handling of edge cases like stale or unavailable URLs, but the schema's exact-URL requirement and the publication_date hint cover the main invocation concerns.

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 the schema already explains all three parameters clearly. The description adds useful workflow context—like 'Use a URL from a previous search_articles or media_radar result' and 'Always link to the original article'—but does not materially extend the semantic meaning of the parameters beyond what the schema provides. A baseline 3 is appropriate.

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 states a specific verb and resource: 'Get the full text of a specific source by its URL,' and clearly differentiates this from search_articles and media_radar by positioning it as the follow-up deep-read tool. It also enumerates what is returned (full body, outlet, author, publication date, sentiment), leaving no ambiguity about its function.

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 provides an explicit WHEN TO USE section and even gives a negative rule: for general analysis, previews from search_articles are usually sufficient, so this tool should only be used for deep dives. This is exactly the kind of alternative-selection guidance agents need.

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

All Perception toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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