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freshcontext

by io.github.PrinceGabriel-lgtm

evaluate_context

Read-only

Evaluate caller-provided candidate context and return decision-ready output. This is the primary FreshContext judgment path: it does not fetch, crawl, scrape, browse, read folders, or call adapters.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nowNoOptional ISO timestamp for deterministic evaluation.
intentYesIntent Profile id, e.g. citation_check, student_research, developer_adoption, job_search, market_watch, business_due_diligence, medical_literature_triage.
profileYesSource Profile id, e.g. academic_research, jobs_opportunities, market_finance, official_docs, local_custom.
signalsYesCandidate context items provided by the caller. FreshContext evaluates these; it does not retrieve them.

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / properties / signals / maxItems
      Added value: +100
  2. Added

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false. The description adds meaningful behavioral context: the tool operates solely on caller-provided context and does not perform any fetching or external calls, which goes beyond the annotation information.

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 two sentences long, front-loaded with the core purpose, and every phrase earns its place. It avoids redundancy and clearly communicates the tool's role and boundaries.

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?

The description adequately covers the tool's essential scope and non-retrieval behavior. With no output schema, it does not detail the return format, but 'decision-ready output' gives a general expectation. The schema is complete, so the description is sufficient for correct invocation.

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%, with all parameters (now, intent, profile, signals) having clear descriptions. The tool description does not add parameter-specific details beyond the schema, so the baseline score of 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 clearly states the verb 'Evaluate' and the resource 'caller-provided candidate context,' with the outcome being 'decision-ready output.' It explicitly distinguishes this tool from fetch/extract siblings by noting what it does not do (fetch, crawl, scrape, browse, read folders, call adapters).

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

Usage Guidelines4/5

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

The description provides clear context: this is the primary FreshContext judgment path and does not retrieve data, implying it should be used when candidate context is already available. It implicitly differentiates from sibling extract/search tools but does not explicitly name an alternative tool for retrieval.

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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TDQS

A3.9/5.0
Disambiguation5/5

Each tool targets a distinct data source (finance, GitHub, Hacker News, etc.), with clear separation and no overlap. An agent can easily distinguish which tool to use for a given source.

Naming Consistency4/5

Tools use a consistent verb_noun pattern with 'extract_' for data extraction and 'search_' for search functions. The outlier 'package_trends' is still descriptive and fits the theme, so the pattern is mostly predictable.

Tool Count5/5

11 tools is well-scoped for a data aggregation server. Each tool serves a clear purpose and the count is neither too sparse nor overwhelming.

Completeness4/5

The server covers a broad range of sources (finance, code, news, social, academia, jobs, packages). Minor gaps like missing Twitter or general news are acceptable given the breadth already provided.

Resources