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by io.github.YGao2005

Check Drift (is my method superseded?)

check_drift
Read-only

Answers 'for my problem, is the method I use superseded — and by what?' over a grounded, entity-resolved knowledge base of textual critique receipts + benchmark-dominance edges (no LLM call at query time). Call with a family (e.g. 'rag', 'peft', 'kvcache') and a method (e.g. 'SnapKV', 'LoRA') to get a verdict: how superseded it is, WHO critiques it (verbatim quotes + the citing paper), WHO beats it on benchmarks (winner, numbers, condition, source paper), and the not-yet-superseded alternatives in the same sub-problem. Omit method to get the whole-family map: most-superseded baselines, competition sub-problems, and the live frontier. Method names are matched case- and spacing-insensitively, with did-you-mean suggestions on a miss. Use this when choosing or reviewing a technique for a known problem area, or to check whether a baseline a paper relies on has been beaten. Does not require a Pro API key. Covers ~10 builder-problem families and growing; the family parameter lists them, or pass family='list' for the live set. Coverage caveat: evidence is drawn only from arXiv benchmark tables, so 'superseded' means a method was beaten in a published comparison (not that it is dead or unusable), production frameworks (LangChain, LlamaIndex, etc.) appear only as baselines and never as winners, and results are a literature signal rather than a deployment recommendation. GROUNDING — how far to trust an individual receipt: every claim passes a deterministic gate against the source paper's raw LaTeX (a critique must carry a verbatim quote shingle found in the source; a benchmark edge must have every one of its numbers present there), so a fabricated quote or table cell cannot enter the KB. What the gate does NOT verify is ATTRIBUTION: the quote is real but its subject may be class-level or a pronoun ('these methods', 'they') rather than the named method, so tying a receipt to one specific method is sometimes an inference. No end-to-end precision number has been measured on this endpoint — read the verbatim quote and its citing paper before repeating a verdict, and cite the source rather than asserting supersession as fact.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax items per list — receipts, dominance edges, frontier (3–50, default 12).
familyNoBuilder-problem family to query, e.g. 'rag' (retrieval-augmented generation), 'peft' (parameter-efficient fine-tuning), 'kvcache' (KV-cache compression). Omit it (or pass an unknown family like 'list') to get the live list of available families to pick from — start here if you don't know the family for a method.
methodNoMethod to check, e.g. 'SnapKV', 'H2O', 'StreamingLLM' (case/spacing-insensitive). Omit to get the whole-family map instead of a single-method verdict.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteNo
foundNo
labelNo
statsNo
familyNo
methodNo
messageNo
summaryNo
verdictNo
frontierNo
beaten_byNo
first_seenNo
descriptionNo
sub_problemNo
suggestionsNo
anchor_arxivNo
anchor_titleNo
sub_problemsNo
criticized_byNo
methods_rankedNo
most_supersededNo
superseded_rankNo
beaten_by_papersNo
available_familiesNo
newer_alternativesNo
critiqued_by_papersNo

Schema Changelog

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

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false; the description adds substantial behavioral detail beyond that. It discloses that there is no LLM call at query time, that matching is case- and spacing-insensitive, that evidence is drawn only from arXiv benchmark tables, that production frameworks never appear as winners, and that the grounding gate checks only receipt authenticity, not attribution accuracy. It even warns that no precision number has been measured and advises citing the source rather than asserting supersession as fact. That is unusually transparent about limitations and is not contradicted by any annotation.

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 long but every paragraph earns its place: the first paragraph defines the tool's question and answer shape, the second covers when to use it and the coverage caveat, and the third explains trust boundaries. It front-loads the most important information. A small deduction because the grounding/attribution paragraph is dense and could be tightened, and the phrasing 'GROUNDING — how far to trust an individual receipt' repeats a caveat that could be absorbed into the previous paragraph. It is still well structured and not padded.

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 complex read-only research tool with no required parameters and a rich output schema, the description covers everything an agent needs: query modes, inputs, output items, limitations, trust model, and source coverage. The output schema exists and is structured, so the description is not responsible for enumerating return fields. The only minor omission is that the description doesn't mention pagination or default behavior for limit, but that is documented in the schema. Overall this is one of the more complete and decision-ready tool descriptions an agent could encounter.

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 description coverage is 100%, so the baseline is 3, but the description adds meaning beyond the property descriptions. It explains that omitting method yields a whole-family map, that omitting family (or passing 'list') yields the live family list, and it illustrates valid values ('rag', 'peft', 'kvcache', 'SnapKV', 'LoRA'). It also clarifies the matching behavior and the role of limit. The one gap is that the description doesn't explicitly define what a 'family' is beyond examples and doesn't enumerate all families, but the schema plus description is sufficient for confident invocation.

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 opens by stating exactly what question the tool answers: 'for my problem, is the method I use superseded — and by what?' and attaches that to a concrete knowledge base. It names the input types (family and method) and the output (a verdict, critiques, benchmark edges, alternatives), and is clearly distinguishable from siblings like find_gaps or ask_library. It covers both single-method and whole-family query modes, which is a level of specificity beyond a generic verb+resource statement.

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 says explicitly when to use this tool: when choosing or reviewing a technique, when checking whether a baseline has been beaten, and when exploring a family via the whole-family map. It also gives a strong 'when-not' signal: it is a literature signal, not a deployment recommendation, so the agent knows not to treat it as a definitive endorsement. The did-you-mean behavior and family='list' route are also spelled out, so an agent knows what to do when uncertain about inputs.

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