Newly FIRST-APPEARING terms from our rolling novelty ledger (Reddit/HN newest posts + the 24/7 idea_intel loop). Surfaces words/phrases/tickers we just started seeing — early-signal detection for new products, memes, projects, or narratives. Each row carries a first_seen date + sample.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Only terms first seen within N days (default 14). | |
| limit | No | Max terms (default 25). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool is non-destructive (a read operation) and mentions output includes 'first_seen date + sample', but does not cover authentication needs, rate limits, or what happens with empty results. It provides basic behavioral context but lacks depth.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences with no fluff. First sentence defines the tool's source and action, second explains the use case, third notes output fields. It is front-loaded and every sentence earns its place, achieving maximum efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description mentions 'first_seen date + sample' which provides minimal return value context. For a two-parameter list tool with good schema coverage and no required params, this is sufficient. However, it could list additional output fields for completeness, but overall it is adequate for an agent to understand the tool's behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (both 'days' and 'limit' have descriptions in the input schema). The description repeats those meanings (days for first-seen window, limit for max terms) without adding additional nuance like valid ranges or behavior at defaults. Baseline 3 is appropriate since the schema already fully documents the parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns newly first-appearing terms from a specific novelty ledger, with explicit mention of sources (Reddit/HN, idea_intel loop) and use case (early-signal detection). The verb 'surfaces' combined with the resource 'Newly FIRST-APPEARING terms' makes the purpose specific and distinct from siblings like 'trending_topics' which likely focus on volume rather than novelty.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for early signal detection of new products, memes, etc., but does not provide explicit when-to-use versus alternatives. Siblings are listed but not contrasted, and no 'when-not' guidance or prerequisites are given. The context is adequate but lacks direct differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: emerging_terms detects new terms, mention_pulse tracks metrics over time for a specific term, trending_topics shows real-time surges, and whats_being_said provides context and themes. No overlap in functionality.
Tool names are in snake_case but vary in structure: 'emerging_terms' and 'trending_topics' follow an adjective+noun pattern, 'mention_pulse' is noun+noun, and 'whats_being_said' is a full phrase. Inconsistency in verb style and pattern reduces predictability.
With 4 tools, the server is well-scoped for social media monitoring. Each tool serves a core function without redundancy, and the count is appropriate for the domain's common use cases.
The tool set covers novelty detection, trend tracking, term analysis, and context gathering. Minor gaps exist (e.g., no direct comparison of multiple terms), but agents can work around them by using mention_pulse repeatedly.