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pulsenetwork

by com.theaslangroupllc.pulse

citepulse

CitePulse: Academic citation analytics for AI research agents, grant offices, and PIs — bibliography verification, retraction detection, paper/author/institution/journal metrics, topic and funder impact scans, and grounded literature briefs. Built on open scholarly infrastructure (OpenAlex, Crossref). All endpoints require x402 payment (USDC on Base mainnet) via the PAYMENT-SIGNATURE header.

Coverage: Global

Endpoints: • ref-check ($0.02): Bibliography verification (flagship) • paper ($0.02): Single-paper lookup • author ($0.02): Author metrics • institution ($0.02): Institution research-output benchmark • journal ($0.10): Journal/venue intelligence • topic-scan ($0.20): Rising-topic research scan • funder-impact ($0.20): Funder research-impact brief • literature-brief ($0.25): Grounded literature synthesis

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
doiNoDOI (preferred, exact match)
langNoResponse language, default en
nameNoAuthor name to search
titleNoPaper title (used only if doi is omitted)
topicNoTopic/field name or keyword
yearsNoLookback window in years, default 3
actionYesWhich endpoint to call. Options: ref-check | paper | author | institution | journal | topic-scan | funder-impact | literature-brief
funderNoFunder name
questionNoThe research question to synthesize
citationsNoComma-separated list of DOIs and/or free-text references, up to 20 items.
compare_toNoOptional second institution name for a side-by-side benchmark
openalex_idNoOpenAlex author ID for an exact, disambiguated lookup

TDQS

A4.2/5.0
Behavior4/5

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

No annotations are provided, so the description bears the full burden of behavioral disclosure. It discloses payment requirements (x402, USDC on Base, PAYMENT-SIGNATURE header), data sources (OpenAlex, Crossref), and global coverage. It does not mention rate limits, return formats, or error behavior, but the disclosed details are important and not present elsewhere.

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 longer than ideal, but the bulleted endpoint list and brief coverage line make it scannable. The first sentence has a slightly promotional tone, yet each section conveys necessary information (purpose, payment, endpoints, pricing). It is appropriately structured for a multi-endpoint tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers multiple endpoints, payment, and data source, but it does not map parameters to specific actions (e.g., which params to use for 'topic-scan' vs 'literature-brief'). There is no output schema or description of return values. Given the tool's complexity (8 actions, 12 params), this omission leaves meaningful gaps in the agent's ability to invoke it 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?

Input schema covers 100% of parameter descriptions, providing a solid baseline. The tool description adds value by explaining what each endpoint does (e.g., 'ref-check: Bibliography verification', 'journal: Journal/venue intelligence'), which enriches the semantics of the 'action' parameter and helps interpret related params like 'citations' and 'funder'.

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's domain and function: 'Academic citation analytics for AI research agents, grant offices, and PIs' and enumerates specific capabilities (bibliography verification, retraction detection, metrics, impact scans, literature briefs). It distinguishes itself from sibling 'pulse' tools by focusing specifically on academic citations and scholarly infrastructure.

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 identifies target users (AI research agents, grant offices, PIs) and lists concrete use cases via endpoints, implying when to use the tool. However, it does not explicitly state when not to use it or name alternatives (e.g., other pulse tools for non-academic domains), so it falls short of a 5.

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

B3.2/5.0
Disambiguation4/5

Each tool has a unique domain prefix (e.g., airdroppulse, alphapulse, arbipulse) making them mostly distinguishable at a glance. A few adjacent verticals like careerpulse vs talentpulse or marketpulse vs dealpulse have overlapping themes, but their descriptions clarify the distinct focus. The utility tools (catalog_search, discover, get_openapi_spec, x402_troubleshoot) are also clearly distinct in role. However, the sheer number of similar 'pulse' names could still cause misselection without reading descriptions.

Naming Consistency4/5

The dominant naming convention is `<domain>pulse` (e.g., climatepulse, cryptopulse, edupulse), which is highly consistent and predictable. Exceptions like catalog_search, discover, get_openapi_spec, x402_troubleshoot, and stateedge break the pattern, but these are few and serve obvious utility purposes. Overall, the convention is clear and easily learnable.

Tool Count2/5

With 80 tools, the server presents an extremely large and potentially overwhelming surface. While each tool represents a distinct intelligence vertical and navigation aids exist (catalog_search, discover, get_openapi_spec), the count far exceeds the typical 3-15 range for coherent agent use and even the 'heavy' 16-25 range. The burden of selecting the correct vertical from 80 options is significant, despite clear naming.

Completeness5/5

The server offers an exceptionally broad and deep coverage of domains, from finance and health to agriculture and gaming. Each vertical includes multiple endpoints that address core operations for its domain, such as search, analysis, comparisons, deterministic checks, and even action-oriented tools like letter generators and physical mail. The presence of free discovery and troubleshooting tools fills potential gaps, leaving no obvious dead ends in the overall tool surface.

Resources