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tle

by io.github.pipeworx-io

Compare Entities

compare_entities
Read-onlyIdempotent

"Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing entities. type="company" pulls LATEST 10-K revenue + net income + cash + long-term debt from SEC EDGAR/XBRL (off-calendar fiscal years handled correctly — AAPL Sep, NVDA Jan, etc.). type="drug" pulls FAERS adverse-event counts, FDA approval counts, active trial counts. Results sorted by primary metric so "largest" / "most" / "biggest" reads off the top of the response. Returns paired data + pipeworx:// citation URIs per entity. Replaces 8–15 sequential lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type: "company" or "drug".
valuesYesFor company: 2–5 tickers/CIKs (e.g., ["AAPL","MSFT"]). For drug: 2–5 names (e.g., ["ozempic","mounjaro"]).

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already cover safety (readOnly, non-destructive). The description adds valuable behavioral context: pulls from SEC EDGAR/XBRL, handles off-calendar fiscal years, sorts by primary metric, and returns citation URIs. This goes beyond the annotation baseline.

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 dense but every sentence contributes. It front-loads with trigger phrases, then explains data sources, edge cases, and output. Slightly long but efficient for the complexity.

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?

For a comparison tool with no output schema, it adequately covers data sources, sorting behavior, output citations, and use cases. It even mentions the 2–5 entity limit and handles fiscal year nuances. Complete enough for an agent to select and invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds meaning by explaining what data is pulled for each type (revenue/net income for companies, FAERS/trial counts for drugs) and gives concrete examples for the values array. This compensates for minimal schema descriptions.

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 tool compares 2–5 companies or drugs side-by-side, with specific verb 'compare' and resource types. It includes trigger phrases and explicitly contrasts with sequential lookups, distinguishing it from sibling tools.

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?

Explicitly says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' and explains when to use company vs drug types. This gives clear guidance on when to choose this tool over alternatives.

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

A3.8/5.0
Disambiguation3/5

Most tools have clearly distinct jobs, but ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research are overlapping query/research entry points—and ask_pipeworx_beta is currently identical to ask_pipeworx. Similarly, ai_visibility_check and scan_competitor_ai_presence overlap by composition. The descriptions are detailed enough to choose correctly with care, but the boundaries are not always crisp.

Naming Consistency3/5

All names use lowercase snake_case, which keeps the surface readable, but the naming conventions are mixed: some are imperative verbs (get_tle, list_recent, validate_claim), some are noun phrases (entity_profile, recent_alerts, pipeworx_trending), and several use domain prefixes without a clear verb (polymarket_arbitrage, polymarket_edges). This is still discoverable naming, but it does not follow a consistent verb_noun pattern.

Tool Count2/5

34 tools is well beyond the well-scoped range, and the vast majority belong to a broad Pipeworx research/prediction-market platform rather than the server's apparent 'tle' satellite theme. Only get_tle, list_recent, and search_satellites directly match the server name. It feels like several tool surfaces aggregated into one server rather than one coherent product.

Completeness3/5

For the satellite TLE theme, NORAD lookup, name search, and recent-catalog listing are covered, but orbit propagation, pass prediction, and historical TLE data are missing. For the broader data-research surface, coverage is rich—query, grounded answers, entity resolution, comparison, validation, subscriptions, and memory are all present—so agents have workable paths for most tasks, but the server's mixed scope creates obvious thematic gaps.

Resources