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Scholar Feed

by io.github.YGao2005

Get Field Orientation

get_field_orientation
Read-only

Returns CANDIDATE FOUNDATIONAL PAPERS for a research topic — cheap retrieval only, no synthesis. Ranks papers by a blend of citation count (0.6 weight, captures importance) and semantic similarity to your topic (0.4 weight). Use this to bootstrap a literature survey or get a fast sense of the landscape. For a synthesized orientation report (key concepts, open problems, reading order), use the /field-guide skill which calls this tool internally. Does not require a Pro API key — no LLM calls are made.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of candidate papers to return (5–30, default 15).
topicYesResearch area to orient on. Be specific for better results. Examples: 'diffusion models for protein structure prediction', 'efficient attention mechanisms for long-context LLMs', 'graph neural networks for molecular property prediction'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoSearch mode actually applied.
noteNo
pageNo
sortNoSearch sort order actually applied.
limitNo
topicNo
totalNoTotal results available for the query. null when the count was skipped (query-less browse, or the count query timed out).
papersNoMatched / returned papers.
directionNoCitation direction (get_citations: citing | cited_by).
not_foundNoRequested IDs that had no match.
next_cursorNoKeyset cursor for the next page, or null when exhausted.

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already mark this as read-only and non-destructive, and the description adds meaningful behavioral context beyond that: it performs no synthesis, makes no LLM calls, and does not require a Pro API key. The ranking weights (0.6 citation count, 0.4 semantic similarity) also disclose exactly how results are scored. This is rich, honest behavioral disclosure.

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 concise and front-loaded with the core result, followed by ranking behavior, use cases, the alternative, and cost characteristics. Every sentence earns its place, and there is no fluff or repetition of schema content. The structure makes the tool easy to scan and act on.

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?

Given only two simple parameters, full schema descriptions, an output schema, and read-only annotations, the description is complete. It covers what the tool returns, how results are ranked, when to use it, when to use the alternative, and its cost/API-key implications. Nothing an agent needs to call it correctly is missing.

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%, so the parameters are already fully documented in the input schema, including defaults, ranges, and examples. The description adds context around ranking behavior but does not need to repeat parameter syntax. Baseline 3 is appropriate because the schema carries the parameter-semantics burden and the description adds modest value.

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 states a specific verb and resource: 'Returns CANDIDATE FOUNDATIONAL PAPERS for a research topic.' It clearly distinguishes itself as 'cheap retrieval only, no synthesis,' setting it apart from synthesis-oriented tools and the /field-guide skill. The ranking formula further clarifies exactly what the tool produces.

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?

The description explicitly says when to use it: 'to bootstrap a literature survey or get a fast sense of the landscape.' It also names the alternative: 'For a synthesized orientation report... use the /field-guide skill,' and notes that the skill calls this tool internally. This gives clear routing guidance between the tool and the more comprehensive option.

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

A4.3/5.0
Disambiguation4/5

Most tools target a distinct resource and action — search vs. saved-library synthesis vs. citation analysis vs. article metadata — and the descriptions explicitly cross-reference one another to reduce confusion. A few retrieval/analysis tools (get_field_orientation, get_foundational_lineage, get_citations, check_drift) have adjacent purposes and could be misselected without reading their descriptions carefully.

Naming Consistency4/5

The overwhelming majority follow a clear verb_noun snake_case pattern: create_watch, delete_watch, list_library, save_paper, annotate_paper, fetch_fulltext, search_papers. Minor deviations like co_author_graph and the interchangeable retrieval verbs (search, find, get, check, ask) create slight inconsistency, but the overall convention is predictable.

Tool Count3/5

27 tools is on the heavy side, but the server covers several coherent subdomains: search/discovery, library/collection management, watches, annotations, and research analysis. The count is justifiable for the broad purpose, though some of the discovery/analysis tools could likely be consolidated or split into a separate server.

Completeness4/5

The tool surface covers the core lifecycle well: search, fetch, save, organize into collections, annotate, watch for new papers, and analyze citations/authors/gaps. Minor gaps exist — there is no collection deletion/rename, no explicit mark-as-read tool, and no unlike operation — but these are workable edge cases rather than blocking omissions.

Resources