Academic paper search, scientific literature, citation analysis, arXiv & semantic related-work.
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Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
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Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 4.3/5 across 9 of 9 tools scored.
Each tool targets a distinct aspect of academic research intelligence—authors, papers, citations, trends, briefs, and protocol info—with clearly different purposes. Descriptions further clarify any potential overlaps, making misselection unlikely.
All tool names use lowercase snake_case, which is consistent. However, the pattern varies between verb_noun (search_papers), noun_noun (author_profile, citation_graph), and adjective_noun (brief_summary, daily_brief), showing minor inconsistency.
Nine tools is well-scoped for a research intelligence server. The count covers essential functions without being overwhelming or sparse, and each tool serves a clear purpose.
The tool set covers core research workflows: search, detail, citation analysis, author profiling, similarity, and trends. Minor gaps exist (e.g., no tool for comparing authors or exporting data), but the surface is largely complete for its domain.
author_profileAInspectAnalyze an author's research profile from OpenAlex — h-index, paper count, total citations, primary field, affiliation, and recent papers. Citation analysis for evaluating academic authors in a literature review.
PAID: $0.01 USDC per query after the daily free allowance (25/day). On a 402, pay the returned Solana memo and re-call with the SAME args plus payment_tx=. An Authorization: Bearer fnet_ key bypasses it.
| Name | Required | Description | Default |
|---|---|---|---|
| agent_id | No | stable id for your agent (scopes the free-tier counter). | |
| author_id | No | an OpenAlex author id (Axxxx) for an exact lookup. | |
| payment_tx | No | Solana tx signature, when re-calling after a 402. | |
| author_name | No | the author's name (best match). |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description takes full responsibility for behavioral disclosure. It transparently explains the paid model ($0.01/query), daily free allowance (25/day), 402 error handling with Solana memo, and optional bypass key. This covers cost, rate limits, and error recovery, going beyond the basic operation.
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 concise with two focused paragraphs: the first stating purpose and outputs, the second detailing payment. It is front-loaded and avoids unnecessary repetition, though combining the payment info could be slightly more efficient.
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 the existence of an output schema and the tool's self-contained nature, the description covers the essential aspects: functionality, outputs, payment model, and error handling. It could mention prerequisites like needing an OpenAlex ID or name, but the schema already provides that. Overall, it is sufficiently complete for an AI agent to use correctly.
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%, so each parameter has a description. The tool description adds extra context for the payment_tx parameter in the 402 flow, but otherwise does not substantially enhance parameter understanding beyond the schema.
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 it analyzes an author's research profile from OpenAlex, listing specific outputs (h-index, paper count, total citations, etc.). It distinguishes itself from sibling tools like search_papers or paper_detail by focusing on the author-level profile.
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 provides context for evaluating academic authors but does not explicitly contrast with sibling tools or state when to use this tool versus alternatives. It includes operational details like payment and error handling, which aid usage but lack explicit conditional guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
brief_summaryAInspectGet the top 5 signals from today's brief as structured JSON — a cheap sample of the full daily_brief. Returns the day's highest-priority items (no prose) so an agent can decide whether to buy the full brief.
PAID: $0.50 USDC (vs the full daily_brief price). Defaults to today (UTC). On a 402, pay the returned Solana memo and re-call with the SAME args plus payment_tx=. An Authorization: Bearer fnet_ key bypasses payment.
| Name | Required | Description | Default |
|---|---|---|---|
| date | No | brief date YYYY-MM-DD (default today, UTC). | |
| agent_id | No | stable id for your agent (scopes the free-tier counter). | |
| payment_tx | No | Solana tx signature, when re-calling after a 402. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses payment requirement, retry flow on 402, default date (today UTC), and the optional auth bypass. It mentions a free-tier counter via agent_id. However, it does not explain rate limits or what happens if the free-tier is exhausted without auth.
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 extremely concise: about 50 words in the main part, no filler, every sentence adds value. It is front-loaded with the purpose, then covers cost, defaults, and retry logic. Perfectly sized for an agent to quickly understand.
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 the complexity of payment and auth, the description covers all essential aspects: purpose, cost, default date, error handling (402), retry instructions, auth bypass, and agent_id identity. An output schema exists, so return values need not be described. It is complete for agent decision-making.
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 coverage is 100%, and each parameter has a description. The tool description adds value by explaining the payment_tx workflow (re-call after 402) and the agent_id purpose (scopes free-tier counter). The date parameter is described with its default behavior.
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 states exactly what the tool does: 'Get the top 5 signals from today's brief as structured JSON — a cheap sample of the full daily_brief.' It uses a specific verb ('Get'), resource ('top 5 signals'), and scope ('today's brief'), clearly differentiating it from the sibling 'daily_brief' which provides the full brief.
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 when to use ('sample of the full daily_brief... decide whether to buy the full brief') and when not to (presumably when you need the full brief). It also provides payment and retry guidance. However, it does not explicitly state alternatives or when to choose other siblings like 'trending_research' or 'search_papers'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
citation_graphAInspectAnalyze the citation graph for a paper via live OpenAlex — list the papers citing it ("citations") or the papers it references ("references"). Citation analysis for tracing influence and prior work in a literature review.
PAID: $0.01 USDC per query after the daily free allowance (25/day). On a 402, pay the returned Solana memo and re-call with the SAME args plus payment_tx=. An Authorization: Bearer fnet_ key bypasses it.
| Name | Required | Description | Default |
|---|---|---|---|
| depth | No | reserved (depth 1). | |
| agent_id | No | stable id for your agent (scopes the free-tier counter). | |
| paper_id | Yes | an OpenAlex work id (Wxxxx) — from a search/detail result's source. | |
| direction | No | "citations" (who cites this) or "references" (what this cites). | citations |
| payment_tx | No | Solana tx signature, when re-calling after a 402. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses payment model ($0.01 USDC per query after free allowance), error handling (402 with Solana memo and re-call instructions), and auth bypass (Authorization header). It also mentions 'live OpenAlex' for data freshness. However, it does not address rate limits or the effect of depth > 1, but overall it provides substantial behavioral insight.
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 concise (about 70 words) and front-loads the main purpose. The payment information is included inline, which could be separated but does not make it overly long. Every sentence adds value.
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 the tool has 5 parameters and an output schema exists, the description adequately covers the core functionality, payment model, and error handling. It does not explain depth > 1 (noted as reserved), but this is a minor gap. Overall, it provides sufficient context for an agent to use the tool correctly.
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 coverage is 100%, so baseline is 3. The description adds meaning beyond the schema: it explains that paper_id is an OpenAlex work ID from a search/detail result, direction values are 'citations' or 'references', agent_id is for free-tier counter scoping, and payment_tx is used for 402 re-calls. This adds useful context.
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's purpose: analyzing the citation graph of a paper via OpenAlex, listing citing or referenced papers. It uses specific verbs ('Analyze', 'list') and resource ('citation graph'), and distinguishes itself from siblings like paper_detail and similar_papers by focusing on citation relationships.
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 provides context for when to use the tool: for citation analysis to trace influence and prior work in a literature review. It does not explicitly state when not to use it or mention alternatives, but the context is clear enough for an agent to select it appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
daily_briefAInspectGet the curated daily academic-intel brief — the day's most significant research in one package, from OpenAlex, arXiv, and PubMed. Includes the top trending research topics, the top cited papers in the window, and notable new papers. Each brief carries a MINT provenance attestation so a buyer can verify it was produced by this server, unaltered.
PAID: $10 USDC per brief. Defaults to today (UTC); a brief expires at the next midnight UTC. On a 402, pay the returned Solana memo and re-call with the SAME args plus payment_tx=. An Authorization: Bearer fnet_ key bypasses payment.
| Name | Required | Description | Default |
|---|---|---|---|
| date | No | brief date YYYY-MM-DD (default today, UTC). | |
| agent_id | No | stable id for your agent (scopes the free-tier counter). | |
| payment_tx | No | Solana tx signature, when re-calling after a 402 (x402 rail). | |
| stripe_token | No | Stripe Checkout Session id (cs_…), when re-calling after paying the Stripe payment link (alternative to x402). Can also be supplied via the X-Stripe-Token header. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool is paid, returns specific content, has an expiration mechanism, and requires payment flow handling. While it doesn't mention rate limits or edge cases, the disclosure is thorough for a read-only paid tool.
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 concise yet comprehensive. It front-loads the main purpose, then efficiently covers payment, expiration, and error handling. Every sentence adds value without redundancy.
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 the presence of an output schema, the description does not need to detail return values. It covers purpose, payment, expiration, and error handling adequately. A minor omission is not explaining what happens if no brief exists for a date, but overall it is complete for the tool's complexity.
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 coverage is 100% with clear parameter descriptions. The description adds value by explaining the payment workflow (402 handling, same args requirement) and the authorization bypass, which go beyond the schema's static descriptions.
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's purpose: getting a curated daily academic-intel brief from OpenAlex, arXiv, and PubMed. It lists specific contents (top trending topics, top cited papers, notable new papers) and mentions a provenance attestation, distinguishing it from siblings like 'trending_research' or 'brief_summary'.
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 explains when to use the tool (daily brief), payment requirements ($10 USDC), expiration behavior, and how to handle a 402 error with payment_tx. It also mentions an authorization bypass. However, it does not explicitly state when not to use it or compare directly to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
mint_infoAInspectGet FoundryNet Data Network info + MINT Protocol details. FREE.
Returns how to attest your agent's literature review / research with MINT Protocol for verifiable on-chain proof, the MINT MCP endpoint, and all ten sister data servers (gov-contracts, brand-intel, patent-intel, financial-signals, weather-intel, cyber-intel, compliance, fact-check, oss-intel, social-intel).
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but description fully explains what is returned (network info, MINT details, attestation guidance, server list). No side effects or auth needed for this read-only tool.
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?
Two concise sentences plus a clear list of servers. No fluff, front-loaded with purpose.
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 zero parameters and an output schema exists, description fully covers what the tool returns including the attestation process and all server names.
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?
No parameters in schema, so description adds no parameter info. But baseline for 0 params is 4, and description adds value by detailing output content.
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?
Description explicitly states 'Get FoundryNet Data Network info + MINT Protocol details' and lists specific returned items, clearly distinguishing from sibling tools focused on papers.
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?
Implied usage (use when you need network info), but no explicit when-not-to-use or alternatives mentioned. The word 'FREE' hints at cost but no exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
paper_detailAInspectGet the full record for an academic paper from OpenAlex, arXiv, or PubMed — abstract, authors, venue, year, citation and reference counts, open-access PDF link, and TLDR when available. FREE. Provide a paper_id, a DOI, or a title.
| Name | Required | Description | Default |
|---|---|---|---|
| doi | No | the DOI. | |
| title | No | a title to look up (best match). | |
| paper_id | No | source paper id (e.g. an OpenAlex Wxxxx or arXiv id). |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It lists return fields and notes TLDR availability, but does not mention error handling, rate limits, or behavior for missing inputs.
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?
Single sentence efficiently communicates purpose, return contents, sources, and input requirements without any filler.
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 the presence of an output schema and three parameters, the description covers primary use cases and sources, but lacks guidance on handling multiple matches or errors.
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 coverage is 100% with minimal descriptions. The tool description adds value by clarifying that only one identifier is needed and by providing format examples for paper_id.
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 uses a specific verb ('Get') and resource ('full record for an academic paper'), lists exact data fields and sources (OpenAlex, arXiv, PubMed), and clearly distinguishes from sibling tools like search_papers or brief_summary.
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 states input options (paper_id, DOI, or title) and notes the tool is free, but does not explicitly contrast with alternatives or specify when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_papersAInspectSearch academic papers across OpenAlex, arXiv, and PubMed for literature reviews — filter by query, year, field, venue, citation count, or open access. Returns title, authors, abstract, and citation count (sorted by citations).
PAID: $0.01 USDC per query after a daily free allowance (25/day). On a 402, pay the returned Solana memo and re-call with the SAME args plus payment_tx=. agent_id scopes your allowance; an Authorization: Bearer fnet_ key bypasses it.
| Name | Required | Description | Default |
|---|---|---|---|
| field | No | field-of-study / category tag (e.g. "Machine learning"). | |
| limit | No | max rows (1-100, default 25). | |
| query | Yes | free-text over title + abstract. | |
| venue | No | journal/conference name, partial match. | |
| year_to | No | ||
| agent_id | No | stable id for your agent (scopes the free-tier counter). | |
| year_from | No | ||
| payment_tx | No | Solana tx signature, when re-calling after a 402. | |
| min_citations | No | minimum citation count. | |
| open_access_only | No | true → only open-access papers. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses that the tool is paid with a free tier, returns 402 status, requires payment_tx on retry, and accepts a bearer key to bypass payment. It also states that results are sorted by citations. It does not mention rate limits beyond the free allowance or other potential behavioral quirks.
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 moderately sized but every sentence adds value: purpose, filters, return info, then payment details. It is front-loaded with the main action. Minor redundancy (repetition of 'filter by' list) could be trimmed, but overall efficient.
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 the presence of an output schema and 80% parameter coverage, the description covers purpose, usage context, payment/error handling, and sorting. It is complete for a multi-source search tool with payment integration. Lacks mention of potential rate limits beyond free allowance or timeout 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 coverage is 80%, and the description adds meaning beyond the schema for most parameters (e.g., field as 'category tag', venue as 'partial match', payment_tx as 'Solana tx signature'). The missing descriptions for year_from/year_to are self-explanatory. The description compensates with payment flow context.
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 verb 'search', the resource 'academic papers', and the scope across three databases (OpenAlex, arXiv, PubMed). It explicitly mentions intended use 'for literature reviews' and lists key filters and return fields, distinguishing it from siblings like 'similar_papers' or 'paper_detail'.
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 provides explicit context on when to use this tool (literature reviews) and includes critical payment and error-handling instructions (402 retry, free allowance, bearer key bypass). However, it does not explicitly mention when not to use it or name alternatives, though the sibling list exists.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
similar_papersAInspectFind related academic papers by semantic similarity — the "related work" finder for a literature review. Give a free-text query OR a paper_id and get the most similar papers via fastembed semantic similarity (pgvector). Premium.
PAID: $0.02 USDC per query after the daily free allowance (25/day). On a 402, pay the returned Solana memo and re-call with the SAME args plus payment_tx=. An Authorization: Bearer fnet_ key bypasses it.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | free-text description of the topic / idea to match. | |
| agent_id | No | stable id for your agent (scopes the free-tier counter). | |
| paper_id | No | a paper id in the dataset to find neighbors of. | |
| payment_tx | No | Solana tx signature, when re-calling after a 402. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose all behavioral traits. It mentions using 'fastembed semantic similarity (pgvector)' and the payment flow, but it does not describe the return format (though output schema exists), rate limits beyond the daily free allowance, or behavior when both query and paper_id are provided. The implied mutually exclusive constraint is useful but not fully explicit.
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 two short paragraphs: the first states the core purpose and input options concisely, the second covers payment details. Every sentence is necessary, no fluff, and the most critical information (purpose) is front-loaded.
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 the tool's moderate complexity (multiple optional parameters, paid model, error handling), the description covers the essential aspects: purpose, input alternatives, payment, error recovery, and authentication bypass. It relies on the output schema for return values, which is acceptable per guidelines. Minor gaps exist (e.g., query length limits) but overall completeness is high.
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%, so the baseline is 3. The description adds value by explaining that query and paper_id are alternatives (mutually exclusive) and detailing the payment_tx parameter's role in the payment flow. This goes beyond the schema's brief descriptions, improving agent understanding of parameter usage.
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 finds 'related academic papers by semantic similarity' and positions it as a 'related work finder for a literature review.' This specific verb+resource pair distinguishes it from siblings like search_papers (exact search) and citation_graph (citation links).
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 explicitly tells users to provide 'a free-text query OR a paper_id', clarifying input modality. It also explains the payment model, daily free allowance, and 402 handling, giving clear context for when and how to use the tool. However, it does not compare directly with sibling tools or state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
trending_researchAInspectFind trending research across OpenAlex, arXiv, and PubMed — the most-cited recent academic papers and the highest-volume topics in a time window, optionally scoped to a field. For tracking emerging scientific literature.
PAID: $0.01 USDC per query after the daily free allowance (25/day). On a 402, pay the returned Solana memo and re-call with the SAME args plus payment_tx=. An Authorization: Bearer fnet_ key bypasses it.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | look-back window (1-365, default 30). | |
| field | No | optional field-of-study filter. | |
| metric | No | "citations" (most-cited) or "volume" (publication volume). | citations |
| agent_id | No | stable id for your agent (scopes the free-tier counter). | |
| payment_tx | No | Solana tx signature, when re-calling after a 402. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses payment model, free allowance, and 402 handling, as well as the metrics returned (most-cited or volume). It does not mention any destructive actions or auth needs beyond the payment, but is transparent about usage limits.
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?
Every sentence serves a purpose, starting with the core function and then the payment details. No unnecessary words, and the information is front-loaded efficiently.
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 the tool's complexity (5 parameters, output schema present), the description covers purpose, usage context, payment behavior, and parameter workflow. It does not need to explain return values due to the output schema, and it is complete for agent invocation.
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?
All parameters are documented in the schema (100% coverage). The description adds value by explaining the payment workflow for payment_tx, which goes beyond the schema description. It provides context that helps the agent understand the parameter usage in practice.
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 finds trending research across three sources, with options for time window and field scope. It uses specific verbs and resources, and distinguishes from sibling tools like search_papers.
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 gives context for tracking emerging literature and details payment handling, but does not explicitly state when not to use this tool versus alternatives. The context is clear, though exclusions are missing.
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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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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