LSP is great for IDEs — and that’s the problem

The Language Server Protocol (LSP) was designed to let editors provide interactive editing features: autocomplete, diagnostics, go-to-definition, etc.

That design assumes an IDE-like client that:

  • Keeps files open in memory (buffers)
  • Streams every edit to the server in real time
  • Maintains a long-lived session with capability negotiation and message envelopes (JSON-RPC + headers)

That’s a perfect fit for an editor.

It’s a mismatch for LLM agents, which are naturally “request/response”, operate under strict token budgets, and often edit code via separate file tools rather than an IDE buffer.

References

Why raw LSP doesn’t suit LLM workflows

1) Too much state synchronization burden

In LSP, the client is responsible for synchronizing document state. Support for textDocument/didOpen, textDocument/didChange, and textDocument/didClose is mandatory, and didChange carries versioning rules and ordered change application.

That’s fine when an IDE is the client. For an LLM agent, it’s brittle:

  • If the agent edits a file on disk, the LSP server won’t know unless the client mirrors those edits via didChange.
  • If versions, ranges, or ordering drift, results become inconsistent (and debugging that through tool calls is painful).

2) “Pointer results” force extra tool calls

Many LSP requests return locations, not the code context itself. For example, definition and references return Location / Location[] (URI + range).

So the agent often has to do a second step:

  1. Ask LSP “where is it?”
  2. Then read files/ranges to learn “what is it?”

That’s extra round-trips, extra latency, extra tokens.

3) Token bloat from verbose payloads

LSP is optimized for machines and editor UI, not token efficiency:

  • JSON-RPC envelopes + capability negotiation
  • Potentially large lists (e.g., completion returns CompletionItem[] / CompletionList with many fields)

LLMs don’t benefit from most of that structure; they benefit from bounded, relevant code and stable identifiers.

4) The “after each edit” problem

IDE workflows assume continuous correctness: the server is always tracking your latest buffer.

LLM workflows are different:

  • Agents do larger edits in chunks
  • They frequently need “is this still compiling?” checkpoints
  • They should not have to worry about fragile line/column offsets and version counters after each patch

What LLM agents actually need (and what LSP doesn’t optimize for)

LLM coding on real codebases needs a tool layer that prioritizes:

  • Fewer tool calls (one call should return enough context to act)
  • Token-efficient outputs (snippets + summaries, not huge JSON)
  • Stable references across steps (so follow-up calls are precise)
  • Compilation/diagnostics loop as a first-class workflow
  • Big repo navigation: call graphs, type hierarchies, impact analysis

LSP can be adapted, but only by building a proxy that basically re-invents an LLM-optimized interface.

What Glider MCP does differently

Glider MCP is explicitly built for AI workflows: a Roslyn-powered MCP server that provides semantic navigation, diagnostics, refactoring, and more—using outputs designed to be consumed by LLM agents.

Key differences vs raw LSP

Glider focuses on “answers”, not “pointers.”
Instead of just returning URIs and ranges, Glider is designed to return bounded source, signatures, and semantically meaningful results that an agent can use immediately.

Stable symbol keys for multi-step workflows.
Glider emphasizes stable keys and precise follow-up calls across tool steps.

State pain is reduced (watching + sync).
Glider can watch a working directory and automatically sync changes, or you can manually sync specific files into its in-memory workspace.

Batching is built in.
You can batch tool calls to reduce round-trips and make agent loops faster.

Example: why Glider needs fewer calls than LSP

Task: “Rename a symbol and fix fallout across the repo”

Raw LSP typically looks like:

  • Maintain a session (initialize, capabilities)
  • Ensure every involved file is opened/synced (didOpen, didChange, versions)
  • Request rename (textDocument/rename) and apply a potentially large WorkspaceEdit
  • Re-run diagnostics + chase more location pointers
  • Repeat… while keeping server state consistent

Glider MCP looks like:

  • Load solution/project
  • Run a semantic rename refactor
  • Sync changes (or rely on auto watching)
  • Re-check diagnostics
  • Apply code fixes
  • Format/organize imports/usings

The difference is not “LSP can’t do it.”
It’s that LSP makes the agent act like an IDE implementation, while Glider makes the agent act like an agent.

On big codebases, agents fail without semantic tooling

On a large solution, an LLM without semantic navigation typically:

  • Misses cross-file relationships
  • Hallucinates symbol names/APIs
  • Can’t reliably answer “where is this used?” or “what breaks if I change this?”
  • Spends tokens grepping and reading irrelevant files

Glider calls this out directly: without tooling, LLMs miss relationships and can’t verify compilation; grep-style search can’t distinguish symbols or understand type relationships.

Reference: https://glidermcp.com/faq

When LSP can still be “good enough”

If you’re operating on:

  • a small repo,
  • a single file,
  • or you already have a robust LSP proxy that truncates payloads, resolves locations to snippets, and handles syncing reliably…

…then LSP can work.

But at that point, you’ve basically built the missing layer: an LLM-optimized API on top of LSP.

Bottom line

LSP is an excellent protocol for IDE features.
LLM agents need something else: fewer calls, fewer tokens, stable references, and a first-class diagnostics/refactor loop.

That’s what Glider MCP is built for.